Method for modeling behavior and health changes

ABSTRACT

One method for supporting a patient through a treatment regimen includes: accessing a log of use of a native communication application executing on a mobile computing device by a patient; selecting a subgroup of a patient population based on the log of use of the native communication application and a communication behavior common to the subgroup; retrieving a regimen adherence model associated with the subgroup, the regimen adherence model defining a correlation between treatment regimen adherence and communication behavior for patients within the subgroup; predicting patient adherence to the treatment regimen based on the log of use of the native communication application and the regimen adherence model; and presenting a treatment-related notification based on the patient adherence through the mobile computing device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/683,867, filed on 16 Aug. 2012, and U.S. Provisional Application No.61/683,869, filed on 16 Aug. 2012, which are incorporated in theirentireties herein by this reference.

TECHNICAL FIELD

This invention relates generally to the field of patient health and morespecifically to a new and useful method for modeling behavior and healthchanges in the field of patient health.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A is a flowchart representation of a first method of theinvention;

FIG. 1B is a flowchart representation of a variation of the firstmethod;

FIG. 1C is a flowchart representation of a variation of the firstmethod;

FIG. 1D is a flowchart representation of a variation of the firstmethod;

FIG. 2A is a flowchart representation of a second method of theinvention;

FIG. 2B is a flowchart representation of a variation of the secondmethod;

FIG. 2C is a flowchart representation of a variation of the secondmethod;

FIG. 2D is a flowchart representation of a variation of the secondmethod;

FIG. 3 is a flowchart representation of variations in accordance withthe first and second methods;

FIG. 4 is a graphical representation of variations in accordance withthe first and second methods; and

FIG. 5 is a graphical representation of variations in accordance withthe first and second methods.

DESCRIPTION OF THE EMBODIMENTS

The following description of the embodiments of the invention is notintended to limit the invention to these embodiments, but rather toenable any person skilled in the art to make and use this invention.

1. Methods

As shown in FIG. 1A, a first method S100 for supporting a patientassociated with a health condition through a treatment regimen includes:accessing a first log of use of a native communication applicationexecuting on a mobile computing device by the patient within a firsttime period in Block S110; receiving a first survey responsecorresponding to the first time period from the patient in Block S112;estimating a first adherence to the treatment regimen by the patientwithin the first time period based on the first survey response in BlockS114; correlating the first log of use of the native communicationapplication with the first adherence to the treatment regimen in BlockS116; accessing a second log of use of the native communicationapplication by the patient within a second time period in Block S120;receiving a second survey response from the patient within the secondtime period in Block S122; estimating a second adherence of the patientwithin the second time period based on the second survey response inBlock S124; correlating the second log of use of the nativecommunication application with the second adherence to the treatmentregimen in Block S126; generating a patient regimen adherence modelincluding the first log of use of the native communication application,the second log of use of the native communication application, the firstadherence, and the second adherence in Block S130; accessing a third logof use of the native communication application by the patient within athird time period in Block S140; estimating a third adherence to thetreatment regimen within the third time period based on the patientregimen adherence model and the third log of use of the nativecommunication application in Block S144; and presenting atreatment-related notification based on the third adherence through themobile computing device in Block S150.

As shown in FIG. 1B, one variation of the first method S100 includes:accessing a log of use of a native communication application executingon a mobile computing device by a patient in Block S110; selecting asubgroup of a patient population based on the log of use of the nativecommunication application and a communication behavior common to thesubgroup in Block S160; retrieving a regimen adherence model associatedwith the subgroup, the regimen adherence model defining a correlationbetween treatment regimen adherence and communication behavior forpatients within the subgroup in Block S162; predicting patient adherenceto the treatment regimen based on the log of use of the nativecommunication application and the regimen adherence model in Block S144;and presenting a treatment-related notification based on the patientadherence through the mobile computing device in Block S150.

As shown in FIG. 1C, another variation of the first method includes:accessing a log of use of a native communication application executingon a mobile computing device by a patient within a period of time inBlock S110; selecting a subgroup of a patient population based on thelog of use of the native communication application and a communicationbehavior common to the subgroup in Block S160; retrieving a regimenadherence model associated with the subgroup, the regimen adherencemodel defining a correlation between treatment regimen adherence andcommunication behavior for patients within the subgroup in Block S162;predicting adherence to the treatment regimen by the patient based onthe log of use of the native communication application and the regimenadherence model in Block S144; extracting a treatment response of thepatient from a patient survey corresponding to the period of time inBlock S112; estimating an efficacy of the treatment regimen in treatinga health condition of the patient according to a comparison between thetreatment response and the adherence to the treatment regimen by thepatient in Block S172; transmitting a notification to a care providerassociated with the patient in response to the efficacy of the treatmentregimen that falls below a threshold efficacy in Block S150.

As shown in FIG. 1D, yet another variation of the first method includes:accessing a log of use of a native communication application executingon a mobile computing device by the patient in Block S110; selecting asubgroup of a patient population based on the log of use of the nativecommunication application and a communication behavior common to thesubgroup in Block S160; retrieving a health risk model associated withthe subgroup, the health risk model defining a correlation between riskof change in a medical symptom and communication behavior for patientswithin the subgroup in Block S162; predicting a risk of change in amedical symptom for the patient based on the log of use of the nativecommunication application and the health risk model in Block S172; andtransmitting a notification to a care provider associated with thepatient in response to the risk of change in the medical symptom for thepatient that exceeds a threshold risk in Block S150.

As shown in FIG. 2A, a second method S200 includes: selecting a subgroupof patients from a patient population in Block S220, patients within thesubgroup exhibiting similar behavioral characteristics and associatedwith a health condition; for a patient within the subgroup, estimatingadherence of the patient to a prescribed treatment regimen during aperiod of time based on survey responses entered by the patient througha corresponding mobile computing device in Block S230; for a patientwithin the subgroup, characterizing communication behavior of thepatient based on use of a native communication application executing ona corresponding mobile computing device by the patient during the periodof time in Block S240; for a patient within the subgroup, correlatingcommunication behavior of the patient with a health status of thepatient in Block S250; estimating an efficacy of the treatment regimenin treating the health condition for patients within the subgroup basedon adherence to prescribed treatment regimens and health statuses ofpatients within the subgroup in Block S260; and generating a treatmentregimen report specific to the subgroup based on the efficacy of thetreatment regimen in Block S270.

As shown in FIG. 2B, one variation of the method includes: selecting apopulation of patients prescribed a treatment regimen for a healthcondition in Block S210; for a patient within the population,characterizing communication behavior of the patient based on use of anative communication application executing on a corresponding mobilecomputing device by the patient prior to initiation of a treatmentregimen and during administration of the treatment regimen to thepatient in Block S240; selecting a subgroup of patients from thepopulation of patients based on similar communication behaviors prior toinitiation of the treatment regimen in Block S220; extracting treatmentresponses from surveys completed by patients within the subgroup duringadministration of the treatment regimen in Block S230; generating atreatment regimen model for the subgroup in Block S280, the treatmentregimen model defining a correlation between communication behavior,treatment responses, and treatment regimen outcomes for patients withinthe subgroup; characterizing communication behavior of a subsequentpatient based on use of a native communication application executing ona corresponding mobile computing device by the subsequent patient inBlock S242; and generating a predicted treatment regimen outcome for thesubsequent patient based on a similarity between communication behaviorof the subsequent patient and communication behavior common within thesubgroup in Block S282.

As shown in FIG. 2C, another variation of the second method includes:selecting a subgroup of patients associated with a health condition froma population of patients, patients in the subgroup exhibiting similarbehavioral characteristics in Block S220; for patients within thesubgroup, characterizing communication behavior of a patient based onuse of a native communication application executing on a correspondingmobile computing device by the patient during the period of time inBlock S240; extracting characteristics of medical symptoms of patientswithin the subgroup from surveys submitted by patients within thesubgroup in Block S230; identifying a relationship between communicationbehaviors of patients within the subgroup, characteristics of medicalsymptoms of patients within the subgroup, and a treatment regimenadministered to patients within the subgroup in Block S232; generating atreatment efficacy model for the subgroup based on the relationship, thetreatment efficacy model defining a correlation between a change incommunication behavior and efficacy of the treatment regimen inalleviating medical symptoms of patients within the subgroup in BlockS280.

As shown in FIG. 2D, yet another variation of the second methodincludes: selecting a subgroup of patients associated with a healthcondition from a population of patients, patients in the subgroupexhibiting similar behavioral characteristics in Block S220; forpatients within the subgroup, characterizing communication behavior of apatient based on use of a native communication application executing ona corresponding mobile computing device by the patient during the periodof time in Block S240; extracting characteristics of medical symptoms ofpatients within the subgroup from surveys submitted by patients withinthe subgroup in Block S230; identifying a relationship betweencommunication behaviors of patients within the subgroup andcharacteristics of medical symptoms of patients within the subgroup inBlock S232; generating a health risk model for the subgroup based on therelationship, the health risk model defining a correlation between achange in communication behavior and risk of change in a medical symptomin Block S280.

2. Applications of the Methods

Generally, the first and second methods S100, S200 function to collectcommunication data of a patient (a user or an at-risk individual) from amobile computing device associated with the patient and to anticipate ahealth status of the patient based on the patient's communication data.The methods can subsequently apply the anticipated health status of thepatient to suggest an action to the patient and/or to inform a nurse,care provider, a family member, a pharmacist, a pharmacologist, aninsurance producer, a hospital, or other health professional or network,etc. with anticipated health-related information of the patient. Themethods can additionally or alternatively implement an anticipatedpatient health status to drive automated or manual targeted interventionof for a patient via a phone call, email, health tip notification,insight, or other electronic communication. The methods can also applyan anticipated health status of a patient to remind the patient tofulfill a treatment, to model the patient's progress through a treatmentprogram or regimen, to predict an outcome of the patient's treatmentprogram, to modify or customize a treatment program for the patient, togenerate a model of treatment regimens and/or treatment regimen outcomesfor a group of similar patients within a population of patients, topredict or monitor a change in patient symptoms or health status change,etc., such as based on volunteered survey data and/or volunteered orpredicted treatment regimen adherence by the patient and/or othersimilar patients.

In one example, over time, the methods collect data related to thepatient's daily phone calls, text messages, and emails, includingfrequency, duration, length of time to respond to inboundcommunications, time-related communication patterns, and/or diversity ofcontacts through one or more native applications executing on asmartphone (or tablet) associated with the patient (e.g., a phone callapplication, a text messaging application, and an email application).Initially, the methods can survey the patient for his health riskassessment, symptom score, and/or degree of current symptoms (e.g., howthe patient is feeling) and then correlate the patient's health status,symptom score etc. to generate a health risk model. For example, themethods can generate a quantitative risk score corresponding to apredicted level of risk of change in a medical symptom for a patientbased on a correlation between patient behavior and a correspondinghealth risk model.

The methods can subsequently implement the health risk model to predicta level of the patient's symptoms at a later time and/or if anotification pertaining to the patient's health should be generatedbased (solely or predominantly) on the patient's communication behaviorthrough a computing device. As in this example, the first method S100can extrapolate a patient health status from patient communication dataand survey responses and combine patient health status with patientcommunication behavior to generate a health risk model, and the firstmethod can later anticipate a change in patient health risk by feedingsubsequent patient communication data into the health risk model todetermine subsequent patient health risk and generate a notification forthe patient accordingly.

In another example, over time, the methods collect data related to thepatient's daily phone calls, text messages, emails, and/or other inboundand/or outbound communications from the patient's mobile computingdevice, including frequency, duration, length of time to respond to aninbound communication, time-related communication patterns, diversity ofcontacts, etc. through one or more native communication applicationsexecuting on a smartphone (or tablet) associated with the patient (e.g.,a phone call application, a text messaging application, and an emailapplication). The methods can thus aggregate and manipulate any of theforegoing data to estimate patient adherence to a treatment regimen(e.g., whether the patient took his medications) and how the patient is‘feeling’ (i.e., degree of symptoms) and subsequently correlate thepatient's treatment adherence and feelings to the patient'scommunication behavior to generate a regimen adherence model. Themethods can subsequently predict how the patient is feeling and/or ifthe patient followed his treatment regimen based on the patient'scommunication behavior through his smartphone. As in this example, thefirst method S100 can extrapolate patient treatment regimen adherencefrom a survey response, combine patient treatment regimen adherence withpatient communication behavior to generate a regimen adherence model,and later anticipate a patient regimen adherence by feeding subsequentcommunication behavior into the regimen adherence model and thengenerate a notification for the patient accordingly.

In a similar example, the methods apply behavior data of the patient,such as prior to and/or during a treatment program or regimen, to selecta regimen adherence model associated with a subgroup of patientsexhibiting communication behaviors similar to those of a particularpatient. By feeding patient communication behavior data into a healthrisk model, the methods can thus predict how the patient is feeling(i.e., presentation of the patient's symptoms) without additionalpatient info (e.g., additional survey results). As in this example, themethods can thus combine survey responses and patient communicationbehaviors to generate patient population subgroup models and lateranticipate a patient health risk—and generate a notificationaccordingly—by feeding subsequent communication behavior of a patientinto the model(s).

In yet another example, the first method S100 applies behavior data ofthe patient, such as prior to and/or during a treatment program orregimen, to select a regimen adherence model associated with thesubgroup exhibiting communication behaviors similar to those of thepatient. By feeding subsequent patient communication behavior into theregimen adherence model, the first method S100 can thus predict how thepatient is feeling (i.e., presentation of the patient's symptoms) at aparticular time and/or anticipate how or when the patient is fulfillinghis treatment regimen, such as by consuming the prescribed medicationsat prescribed times and at prescribed dosages, without additionalpatient info (e.g., additional survey results). As in this example, thisvariation of the first method S100 can thus extrapolate patienttreatment regimen adherence from a survey response, combine patienttreatment regimen adherence with patient communication behavior toselect a patient population subgroup and a related regimen adherencemodel, and later anticipate a patient regimen adherence by feedingsubsequent communication behavior into the regimen adherence model andthen generate a notification for the patient accordingly.

In still another example, the methods implement behavior data of apatient to select a health risk model associated with a particularsubgroup exhibiting communication behaviors similar to those of thepatient. By feeding subsequent patient communication behavior data intothe health risk model, the methods can this predict how the patient isfeeling (i.e., presentation of the patient's symptoms) at a particulartime and/or anticipate how or when the a notifications indicatingpatient risk should be transmitted to a care provider, all withoutnecessitating additional patient information (e.g. additional surveyresults).

In a further example, the first method S100 applies behavior data of thepatient, such as prior to and/or during a treatment program or regimen,to select a regimen adherence model associated with the subgroupexhibiting communication behaviors similar to those of the patient. Byfeeding subsequent patient communication behavior into the regimenadherence model, the first method S100 can thus predict how the patientis feeling (i.e., presentation of the patient's symptoms) at aparticular time and/or anticipate how or when the patient is fulfillinghis treatment regimen, such as by consuming the prescribed medicationsat prescribed times and at prescribed dosages, without additionalpatient info (e.g., additional survey results).

In another example, the second method S200 collects communication datafrom multiple patients, groups patients according to identifiedcommunication behaviors, and anticipates progress through a treatmentprogram and the effects of the treatment program on patients with aselected group. Rather than relying on data entered manually by patientsand/or care providers, estimating patient activity or action from motiondata (which can be imprecise, require substantial computing powers, andlack suitable precision for the health care space), etc., the secondmethod S200 can instead generate models linking treatments (e.g.,pharmacotherapy regimens, physical therapy programs) to symptompresentation and final health condition outcome for patients based onhow, when, and/or with whom, etc. patients communicate over time (e.g.,on a daily or four-hour basis). In this example, the second method S200can thus select a subgroup of patients within a patient population basedon communication behaviors of the patient population, estimate treatmentregimen adherence within the subgroup based on survey responsesvolunteered by patients within the subgroup, estimate the health statusof patients within the subgroup based on communication behaviors of thepatients, and combine treatment adherence and communication behavior ofpatients within the subgroup to estimate the efficacy of the treatmentregimen specifically for the subgroup of patients.

In the foregoing example, the second method S200 can further apply suchmodels to particular patients to anticipate how a patient will respondto a treatment regimen and/or to customize a treatment regimen for thepatient based on similarities and/or differences between the patient'scommunication behaviors and communication behavior of other patients(e.g., previous or current patients with similar diagnosed healthconditions and/or prescribed treatment regimens).

The first and second methods S100, S200 can therefore derive meaningfulhealth-related data for individual patients and/or groups of patientsfrom patient communication behaviors extrapolated from phone call, textmessage, email, and/or other communication data collected passivelythrough mobile computing devices associated with the patient(s). Suchderived health-related data can be implemented within a patient's mobilecomputing device to privately guide a patient through a treatmentprogram by a care provider to anticipate risk of increasing medicalsymptoms or risk of a change in health status of the patient, by adoctor to monitor a patient's progress or to modify a prescription, by anurse to identify a need to provide manual support to the patient,and/or by a pharmacologist to research drug therapies and drug responseswithin a population, etc. For example, Blocks of the first and secondmethods S100, S200 can be implemented to collect patient data for anexperiment, a research study, a commercial launch, a marketing study, apatient community study, such as on the recommendation of a healthcareprovider (e.g., a doctor or a medical institution (e.g., a hospitalcenter) to improve patient care. In this example, the first and secondmethods S100, S200 can interface with a patient-facing interface, adoctor-facing interface, a nurse-facing interface, and/or apharmacologist-facing interface, etc. to deliver notifications andrelated data to the patient, doctor, nurse, pharmacologist, and/or othercare provider directly or indirectly associated with the patient. Inanother example, the first and second method S100, S200 can beimplemented on a computing device associated with a particular patientas a personal heath-tracking tool.

In one implementation, the first and second methods S100, S200 interfacewith a native data collection application executing on a patient'smobile computing device (e.g., smartphone, tablet, personal dataassistant (PDA), personal music player, vehicle, etc.) to retrievepatient communication data. For example, the native data collectionapplication can be installed on the patient's mobile computing device,execute substantially continuously while the mobile computing device isin use and/or “ON,” record times, durations, and contact types (e.g.,family, friend, coworker, business associate) of each inbound andoutbound communication from the patient's mobile computing device. Themobile computing device can then upload this data to a remote database,such as in real-time, every hour, at the end of each day, etc. over anInternet connection, and the first and/or second method, implemented ona computer network (e.g., the “cloud”), can retrieve the patient'scommunication data from the remote database, analyze the patient'scommunication data to anticipate the patient's symptoms and/or therapyadherence, and generate a notification for the patient, and thepatient's mobile computing device can download and subsequently displaythe notification for the patient, actions that can be handledautomatically and in the background by the native data collectionapplication or an alternative patient-facing native applicationexecuting on the mobile computing device. The computer network canadditionally or alternatively generate a patient regimen adherencemodel, a patient outcome model, a patient behavioral model, a healthrisk model etc. and transmit any one or more models to a doctor, anurse, a pharmacologist, a therapist, etc.

Therefore, Blocks of the first and second methods S100, S200 can beimplemented on one or more computer systems, such as a cloud-basedcomputer system (e.g., Amazon EC3), a mainframe computer system, agrid-computer system, or any other suitable computer system. Blocks ofthe first and second methods S100, S200 can collect patient data fromone or more devices over the Internet, such as communication datadirectly from a natively application executing on the patient'ssmartphone and/or motion data from a wearable sensor collected by thepatient's smartphone and then uploaded to a remote database over anInternet connection. Collection, manipulation, and transmission ofpatient data in Blocks of the first and second methods S100, S200 canfurther adhere to health-related privacy laws, such as by privatizing oranonymizing patient data and transmitting encrypted or privatenotifications—such as pertaining to a patient's therapy adherence—to thepatient and/or a doctor, a nurse, a pharmacologist, a researcher, etc.associated with the patient.

As in the foregoing implementation, when a patient installs and/orauthorizes collection and transmission of personal communication datathrough such a native data collection application, the nativeapplication can prompt the patient to create a profile or account. Theaccount can be stored locally on the patient's mobile computing deviceand/or remotely on the computer network and can contain a name, age,gender, location, occupation, list of health conditions, list of currenthealth-related treatments and medications, medical history, a primarycare physician (including name, office, contact information, etc.),health insurance information, prescription insurance plan, localpharmacy information, or other demographic and/or health-relatedinformation, any of which can be added by the patient, a family member,a doctor, a nurse, or other individual associated with the patientand/or retrieved from a medical, insurance, social network, or otherrelated database. The first and second methods S100, S200 can alsoupdate the patient's account with additional patient information overtime, such as presentation of symptoms, estimated patient treatmentadherence, and/or predicted treatment regimen outcome for the patient.

Blocks of the methods can function to qualify and/or quantify acorrelation between a health outcome or symptom risk and observedbehavior changes for a patient based on data gathered passively from apatient's mobile computing device (e.g., mobile phone, smartphone and/ortablet, etc.) substantially without manual data entry from the patient,a doctor, a nurse, a pharmacist, etc. Blocks of the methods cansimilarly quantify a correlation between a patient health status and anobserved patient behavior gathered from the patient's mobile computingdevice and further combine passive behavior data collection withself-reported patient data (i.e., survey responses) to enhance thequality of a health-related model, extrapolated health-relatednotification triggers, patient inferences, patient symptom riskpredictions, etc. for an individual patient and/or for a subgroup ofpatients. In particular, the first and second methods S100, S200 canidentify a relationship between a health outcome and acommunication-related behavior change (e.g., a change in the frequencyof phone calls, text messages, and emails over a preset period of time),such as changes in a patient's communication behavior from prior toadministration of a treatment to during and/or after administration ofthe treatment for the patient.

The first and second methods S100, S200 can also identify a correlationbetween health outcome and patient activity data (i.e., accelerometerand gyroscope data from a corresponding mobile device or wearablesensor), local environmental data, patient location data, patient surveydata, etc. The first and second methods S100, S200 can then implement anidentified relationship between a (communication) behavior change and achange in a patient's health or quality of life over a period of time toanticipate a change in patient health risk, trigger or target anautomated or manual intervention (e.g., an automated notification) toassist the patient through a treatment regimen, anticipate symptomrelapse and estimate risk of hospital readmission for the patient, etc.The methods can similarly implement the foregoing identifiedrelationship to identify categories, clusters, or subgroups of similarpatients within a patient population, to modify or customize treatmentregimens or patient health risk models for particular patient clusters,to prescribe a customized or subgroup-specific treatment regimen to asubsequent patient, to assist a care provider in patient triage, tosupport or justify adjustment to health care utilization, to comparehealth outcomes (e.g., admission/readmission rates and health careutilization) within and/or across a patient population, etc. The methodscan thus function to improve health outcomes for a particular patientand/or within a subgroup of a patient population and to improve healthcare utilization.

Generally, the first and second methods S100, S200 can function toqualify and/or quantify a correlation between a health outcome relatedto a treatment, therapy, and/or medication, etc. and observed behaviorchanges based on data gathered passively from a patient's mobilecomputing device (e.g., mobile phone, smartphone and/or tablet, etc.)substantially without manual data entry from the patient, a doctor, anurse, a pharmacist, etc. The first and second methods S100, S200 cansimilarly quantify a correlation between a patient health status and anobserved patient behavior based on patient data gathered from thepatient's mobile computing device and combine passive behavior datacollection with self-reported patient data (i.e., from surveys) toenhance the quality of a treatment regimen, inferences, and treatmentpredictions for a patient and/or a subgroup of patients. In particular,the first and second methods S100, S200 can identify a relationshipbetween a health outcome and a communication-related behavior change(e.g., phone calls, text messages, emails), such as changes in apatient's communication behavior from prior to administration of atreatment to during and/or after the treatment for an individualpatient. The methods can also identify a change in composite work-lifebalance for the patient, such as based on time spent by the patient inwork-related phone calls or sending work-related emails or at a physicaloffice or work location relative to a total communication time of thepatient, a total patient waking time per day, or an amount of time spentat home. The methods can thus correlate a change in patient compositework-life balance with a declining patient health status or increasedpatient health risk (e.g., for recurring or symptoms of increasedseverity) based on the health risk model. Similarly, the methods cancompute a composite quality of life score for the patient, based on timespent in phone calls and at physical locations related to social events,familial and work phone calls, and work locations and transitionsbetween such locations, and the methods can correlate a change incomposite quality-of-life with a declining patient health status orincreased patient health risk based on the health risk model.

The first and second methods S100, S200 can additionally determine acorrelation between health outcome and patient mobile phone usage (i.e.screen unlocks, mobile application use) derived from an operating systemor the task manager executing on the patient's mobile computing deviceto determine periods of patient activity, hyper-activity (e.g. frequentunlocks late at night when the patient is unable to sleep), andinactivity. The first and second methods S100, S200 can then implementan identified relationship between a mobile usage behavior change and achange in a patient's health or quality of life over a period of timebased on a corresponding health risk model.

The first and second methods S100, S200 can also determine a correlationbetween health outcome and patient activity data (i.e., accelerometerand gyroscope data from the mobile computing device or external wearablesensor), local environmental data, patient location data, patient surveydata, etc. The first and second methods S100, S200 can then implement anidentified relationship between a (communication) behavior change and achange in a patient's health or quality of life over a period of time toanticipate a treatment efficacy and/or patient response to a treatment,estimate a patient's treatment adherence (i.e., compliance), determinepatient satisfaction in a treatment, trigger or target automated ormanual interventions (e.g., automated notifications) to assist a patientthrough a treatment, anticipate symptom relapse and estimate risk ofhospital readmission. The first and second methods S100, S200 cansimilarly implement such an identified relationship to identifycategories, clusters, or subgroups of similar patients within a patientpopulation, to modify or customize treatment regimens or notificationsfor particular patient clusters, to prescribe a customized orsubgroup-specific treatment regimen to a subsequent patient, to assist acare provider in patient triage, to support adjustment health careutilization, to compare treatment outcomes (e.g., admission/readmissionrates and health care utilization) within and/or across a patientpopulation, etc. to improve treatment outcomes, therapy efficacy, andadherence to a treatment program for a particular patient and/or withina subgroup of a patient population, and health care utilization.

Though the following description describes the inventions particularlyin the context of monitoring and/or estimating treatment adherence,Blocks of first and second methods S100, S200 can implement similarfunctionality and techniques to identify patient risk and assesstreatment efficacy.

3. Behavior Data

Block S110 of the first method S100 recites identifying a first log ofuse of a native communication application executing on a mobilecomputing device by the patient within a first time period. Block S120and Block S140 similarly recite identifying a second log and a third logof use of the native communication application by the patient within asecond and a third time period, respectively. Generally, Blocks S110,S120, and S140 function to unobtrusively collect and/or retrievecommunication-related data from a patient's mobile computing device,such as through integration within or by interfacing with a native datacollection application to collect patient data, as described above. Forexample, the native data collection application can launch on thepatient's mobile computing device as a background process that gatherspatient data once the patient logs in to his account. In particular,Block S110, etc. collects communication data and/or native communicationapplication usage data, generated by the patient's mobile computingdevice, to identify how and how often (i.e., with what frequency) thepatient interacts with and communicates with other individuals throughphone calls, e-mail, instant messaging, an online social network, etc.

In one implementation, Block S110, etc. collects phone call-relateddata, including a number of sent and/or received calls, call duration,call start and/or end time, location of patient before, during, and/orafter a call, and number and times of missed or ignored calls. BlockS110, etc. can also collect text messaging (e.g., SMS test messaging)data, including number of messages sent and/or received, message length,message entry speed, efficiency, and/or accuracy, time of sent and/orreceived messages, and location of the patient when receiving and/orsending a message. Block S110, etc. can collect similar types of data ontextual messages sent through other communication venues, such as publicand/or private textual messages sent to contacts of the patient throughan online social networking system, reviews of products, services, orbusinesses through an online ranking and/or review service, and/or anyother text-based communication generated by the patient and communicatedto another individual and/or computer network.

Block S110, etc. can further collect location data of the patientbefore, during, and/or after (or in the absence of) communication withanother individual (e.g., a phone call) and/or computer network (e.g., asocial networking message), such as by retrieving a GPS location from aGPS sensor within the patient's mobile computing device, estimating thelocation of the patient's mobile computing device through triangulationof local cellular towers, or identifying a geo-located local Wi-Fihotspot, etc., during a phone call. Block S110, etc. can apply this datato track patient behavior characteristics, such as patient mobility,periods of patient isolated, patient work-life (e.g., balance based ontime spent at specific locations), etc. Block S110, etc. can alsocollect patient location data before, during, and/or after communicationwith another individual, such as via a phone call and/or over a computernetwork (e.g., with a social networking message), and merge patientlocation with patient communication (or other) data. Block S110, etc.can therefore track the patient's mobility during a communication. BlockS110, etc. can additionally or alternatively collect data pertaining toindividuals in contact with the patient during the first period of time,etc., such as an individual's location during a phone call, phonenumber, contact duration and/or type with the patient, relationship tothe patient or patient contact group (e.g., top contact, spouse, familymember, friend, coworker, business associate, etc.) specified by thepatient or learned from previous patient communications, etc.

Blocks S110, S120, etc., can further capture mobile usage data likescreen unlocks and mobile application usage, such as by retrieving usageinformation from mobile operating system logs or task manager on themobile computing device. Blocks of the methods can therefore trackvariations and periods of activity and inactivity for a patient throughdata automatically collected on the patient's mobile computing device,such as to estimate extended periods when the patient was hyperactive onthe device or not asleep.

In one implementation, Block S110, etc. also collects or retrievespatient physical activity- or physical action-related data (e.g.,accelerometer and gyroscope data), local environmental data, patientnutrition or diet-related data, etc. such as recorded through sensorswithin the patient's mobile computing device or through a wearable orother peripheral device in communication with the patient's mobilecomputing device. For example, a wireless-enabled scale, blood pressuresensor, and a pulse-dosimeter sensor can transmit the patient's weight,blood pressure, and blood oxygen level to the patient's mobile computingdevice, and Block S110 can add this data to the patient's account tofurther augment patient behavior data.

Block S110 can subsequently aggregate phone, text message, email, socialnetworking, and/or other patient communication data for a particularperiod of time into a qualitative and/or quantitative feature for thepatient for the particular time period. The feature can be specific to aday, a week, a month, a day period (e.g., morning, afternoon, evening,night), a time block during a day (e.g., one hour), a specificcommunication action (e.g., a single phone call, a set of communicationactions of the same type (e.g., a set of phone calls within a two-hourperiod), all communications within a period of time, etc.). For example,Block S110 can generate a weighted composite of the frequency, duration(i.e., length), timing (i.e., start and/or termination), and contactdiversity of all outgoing voice (e.g., phone call) communications and afrequency, length, and timing and/or response time to (i.e., time toaccept) incoming voice communications within the first period of timethrough a phone call application executing on the patient's mobilecomputing device. Block S110 can additionally or alternatively assessincoming and/or outgoing textual communications from a textual messagingapplication executing on the mobile computing device. Block S110 canalso generate a quantitative assessment of a frequency of, a durationof, and a response time to both incoming and/or outgoing phone calls andtextual communications to the mobile computing device during the firsttime period as a single qualitative and/or quantitative featurecorresponding to the first period of time. Block S120 and/or S140 canimplement similar methods to generate a feature for the second period oftime and third period of time, respectively.

Blocks S112, S122, etc. can further extract features based on voicecommunications, textual communications, mobile application activityusage, location data, etc., which can be based on variance, entropy, orother mathematical and probabilistic computations of basic data, such asa composite activity score, a composite socialization score, a work-lifebalance score, a quality-of-life score, etc.

In one example, Block S110 implements machine learning, data mining, andstatistical approaches to process patient communication data intorelevant patient communication behavior features (e.g., data points).Block S110 can implement similar techniques to similarly process patientmotion data, local environmental, and other automatically/passivelycollected data.

Block S240 of the second method S200 recites, for patients within thesubgroup, characterizing communication behavior of a patient based onuse of a native communication application executing on a correspondingmobile computing device by the patient during the period of time. BlockS240 can thus implement functionality similar to Block S110, Block S120,etc. to extrapolate patient communication behavior from phone calls,text messages, social networking communications, emails, and/or othercommunications originating and/or terminating through one or morecommunication applications executing on one or more mobile computingdevices associated with one or more patients. In one example, Block 240characterizes communication behavior of multiple patients based on useof native communication applications executing on corresponding mobilecomputing devices by the patients both prior to initiation of and duringadministration of an action item specified in an automated notificationdirected to the patient and/or an associated nurse, doctor, careprovider, etc. In another example, Block S240 characterizescommunication behavior of multiple patients based on use of nativecommunication applications executing on corresponding mobile computingdevices by the patients both prior to initiation of similar treatmentregimens and during administration of the treatment regimens.

Block S242 of the second method, which recites characterizingcommunication behavior of a subsequent patient based on use of a nativecommunication application executing on a corresponding mobile computingdevice by the subsequent patient, can also implement such functionalityto characterize communication behavior of a new (i.e., subsequent)patient. For example, Block S242 can pass the communication behaviorcharacterization of the new patient to Block S282, and Block S282 candetermine a similarity between the new patient and a subgroup ofpatients based on communication behaviors of the two and, from thisdetermined similarity and an effectiveness of the health-relatednotifications within the subgroup, generate a health-relatednotification for the new patient. In another example, Block S242 passesthe communication behavior characterization of the new patient to BlockS282, wherein Block 282 determines a similarly between the new patientand a subgroup of patients based on communication behaviors of the twoand, from this determined similarity, predicts an effectiveness in atreatment regimen for the new patient based on the effectiveness of thetreatment regimen within the subgroup.

However, Blocks S110, S120, S140, S240, S242, etc. can function in anyother way to identify and/or characterize use of a native communicationapplication by a patient within a period of time.

4. Surveys

Block S112 of the first method S100 recites receiving a first surveyresponse corresponding to the first time period from the patient. BlockS122 similarly recites receiving a second survey response from thepatient within the second time period. Generally, Block S112, etc.functions to prompt the patient to self-report additional health-relateddata that can be implemented within the first and/or second method S200to qualify patient communication data, such as to teach a patientregimen adherence model relating patient communication to patienttreatment adherence and/or patient symptom presentation. For example,Block S112 transmits a survey to the patient's mobile computing device,the native data collection application executing on the mobile computingdevice opens the survey and prompts the patient to enter relevant data,and Block S112 receives patient responses to the survey from the mobilecomputing device once the survey is complete. In this example, BlockS170 can function to extract a treatment response of the patient from acorresponding survey.

Block S112 can generate the survey that includes prompts to enter a painlevel (e.g., on a body-location-specific basis), presentation ofsymptoms (e.g., on a health-condition-specific basis), adherence to atreatment regimen (e.g., if and when the patient took a prescribedmedication of some dosage), changes in presentation of symptoms after atreatment, how the patient “feels” generally, experience with atreatment and corresponding effect, a mood, a sleep quality orwakefulness, etc. The survey can also prompt the patient to supplyinformation related to a diagnosed disease or condition, such as majordepressive disorder, diabetes, or chronic obstructive pulmonary disease.Alternatively, Block S112 can retrieve relevant patient health data froma medical record, history, or profile of the patient. In one example,Block S112 can import a patient medication record from an ElectronicMedical Records (EMR) system hosted by a healthcare provider or healthinsurance company, such as via a supported Application ProgrammableInterface (API).

Surveys can be presented to the patient on the mobile computing deviceat preset or patient-selected launch times and/or frequency. Forexample, Block S112 can prompt the patient to fill out a two-questionsurvey every morning at 9:00 or after each meal, including whether hehas taken prescribed medication(s) and his overall satisfaction with thetreatment in mitigating symptoms. Alternatively, presentation of surveyscan be triggered by a determined patient behavior or symptom change. Inone example, Block S112 can trigger presentation of a survey to thepatient in response to a disparity in actual and anticipated patientcommunication behavior, such as significantly more or significantly lessphone call time by the patient than expected based on past patientcommunication behavior. In another example, Block S112 can triggerdelivery of a pain survey launched within the application either at apre-determined time and/or once a period of time (e.g., 72 hours)transpires without a detected change in the patient's location (e.g.,the patient has not left his house) and/or without a detected phone callinitiated by the patient.

Surveys can include a single or combination of question-and-answertypes, such as multiple choice questions each with a single answeroption, multiple choice questions each with multiple answer options,textual or numerical manual entry, slider (e.g., for easy number orlevel selection from a range), icon animation selection (e.g., iconsindicating different intensities or symptoms), etc., as shown in FIG.1A. For example, a survey can include a Patient Health Questionnaire(PHQ9), a WHO Wellbeing Index (WHO5), or a one-question Pain RatingScale question.

Block S112 can additionally or alternatively generate a prompt targetedat a family member, care providers, health organization, etc. associatedwith the patient. For example, Block S112 can prompt and collect surveyresponses from a nurse, including manually-entered patient symptom andtreatment data. Alternatively, Block S112 can retrieve this informationfrom the personal mobile computing device of a family member of thepatient. Block S112 can similarly prompt a pharmacist or pharmacologistassociated with the patient's treatment to enter patient prescriptiondata directly into a survey. Alternatively, Block S112 can retrieve thisdata automatically from an associated pharmacy database. Yetalternatively, Block S112 can collect patient treatment data from athird-party system or device used by the patient or associated careprovider, such as an Internet-enabled pillbox with embedded sensors.

Block S112 can further function to compensate the participant forcompleting a survey, such as with cash, a gift card, a pharmacydiscount, a health insurance discount, etc., such as in an applicationof the first and/or second method S200 in which patient data is suppliedto pharmaceutical researches to generate treatment regimens.

Block S112 can be implemented within the native data collectionapplication, on a computer network in communication with the mobilecomputing device (e.g., via an Internet connection), or in any othersuitable way. Block S122, etc. can implement similar techniques orfunctions, though Block S112, Block S122, etc. can function in any otherway to collect survey responses corresponding to various time periodsfrom the patient.

5. Data Storage

The first and second methods S100, S200 can store data locally on thepatient's mobile computing device and/or in a remote database on acomputer network. For example, private health-related patient data canbe stored temporarily on the patient's mobile computing device in alocked and encrypted file folder on integrated or removable memory. Inthis example, the patient's data can be encrypted and uploaded to theremote database once a secure Internet connection is established.However, patient data can be stored on any other local device or remotedata in any other suitable way and transmitted between the two over anyother connection via any other suitable communication and/or encryptionprotocol.

6. Treatment Adherence

Block S114 of the first method S100 recites estimating a first adherenceto the treatment regimen by the patient within the first time periodbased on the first survey response. Block S124 of the first method S100similarly recites estimating a second adherence of the patient withinthe second time period based on the second survey response. Furthermore,Block S230 of the second method S200 recites, for patients within thesubgroup, estimating adherence of a patient to a prescribed treatmentregimen during a period of time based on survey responses entered by thepatient through a corresponding mobile computing device. Generally,Blocks S114, S124, S230, etc. function to extract the patient adherenceto a treatment regimen over time from corresponding survey responsescollected in Blocks S112, S122, etc. For example, a survey can includean explicit inquiry into if, when, and in what dosage the patient took amedication specified in a pharmacotherapy regimen and/or if the patientcompleted and the duration of a physical therapy session, and Block S114can identify the patient's response to the prompt, compare the patient'sresponse to a treatment regimen assigned to the patient for thecorresponding time period, and thus determine if and/or to what extentthe patient fulfilled the treatment regimen for the corresponding timeperiod. Block S114 can subsequently pass the degree of patient adherenceto the treatment regimen to Block S116. Block S124 and Block S230, etc.can implement similar functionality.

Block S116 of the first method S100 recites correlating the first log ofuse of the native communication application with the first adherence tothe treatment regimen, and Block S126 similarly recites correlating thefirst log of use of the native communication application with the firstadherence to the treatment regimen, and Block S126 recites correlatingthe second log of use of the native communication application with thesecond adherence to the treatment regimen. Generally, Block S116, BlockS126, etc. function to define a relationship between a degree of patientadherence to a treatment regimen and a quality and/or quantity ofcommunication (i.e., communication behavior) of the patient for acorresponding period of time.

In one implementation, Block S116 characterizes patient-providedtreatment adherence data from the first survey and associates thetreatment adherence characterization with the communication behaviorcharacterization from Block S110. At a later time when the patient'scommunication behavior mimics the communication behavior during thefirst period, the first method S100 can thus anticipate the patient'streatment adherence at the later time to mimic the patient's treatmentbehavior during the first time period. In one example, Block S116correlates a log of use of a first set of outgoing voice communicationsfrom a phone call application during the first period with conformity tothe treatment regimen by the patient, and Block S126 correlates afrequency of a second set of outgoing voice communications within thephone call application with neglect of the treatment regimen by thepatient, as shown in FIG. 4, wherein the log of use of the first set ofoutgoing voice communications is greater than the frequency of thesecond set of outgoing voice communications.

Block S116 can additionally or alternatively correlate adherence to thetreatment regimen and/or patient communication behavior to patienthealth status, such as presentation of patient symptoms. In one example,Block S116 extracts a patient symptom level from the first survey andpresentation of patient symptoms (e.g., how well the patient is feeling)with patient communication behavior (e.g., how, when, and who thepatient contacts during a period of time). In this example, Block S116can determine that a period during which the patient sends a low volumeof text messages to a large breadth of contact types and engages in longphone conversations with a limited number of contacts corresponds tominimal symptom presentation, and Block S126 can determine that a periodduring which the patient sends a low volume of text messages to a lownumber of unique contacts and engages in short phone conversations witha single contact corresponds to a high degree of symptom presentation.In this example, Block S116 can also correlate minimal symptompresentation with a certain degree of treatment adherence during thefirst period (e.g., the patient followed the treatment adherenceproperly), and Block S126 can further correlate higher degree of symptompresentation with a lesser degree of treatment adherence during thesecond period (e.g., the patient missed a treatment or took an improperdosage).

The first method S100 can repeat elements of Blocks S110, S112, S114,S116, etc. to generate a set of communication behavior-treatmentadherence (and health status) features for the patient over time, andthese features can be fed into Block S130, Block S160, Block S144, BlockS150, Block S172, and/or Block S180, etc. to enable furtherfunctionality.

Block S250 of the second method, which recites correlating communicationbehavior of a patient with health statuses of patients within thesubgroup, can implement similar functionality to generate communicationbehavior-treatment adherence (and health status) features within thesubgroup of patients. For example, Block S250 can pair anonymizedcommunication data from various patients exhibiting similar behaviorsover time, such as before, during, and after a treatment regime, withsurvey responses from corresponding patients at corresponding times tooutput a collection of features specific to the subgroup. Block S250 canthus pass this collection of features to Block S280. Block S280 can thusimplement the collection of features to identify trends or patternsbetween communication behavior, treatment adherence, and presentation ofsymptoms across the population and generate a corresponding treatmentregimen model for the subgroup, as described below.

7. Subgroups

Block S160 of the first method S100 recites selecting a subgroup of apatient population based on the first log of use of the nativecommunication application and a communication behavior common to thesubgroup. Generally, Block S160 functions to identify one or morepatients within a patient population exhibiting characteristics similarto those of the (current, new, or subsequent) patient, such thatsubsequent Blocks of the first method S100 can apply preexistingcommunication behavior data, treatment adherence data, health statusdata, etc. and corresponding characterizations, patterns, models, etc.of this data to inform and/or improve manipulation of the patient'shealth-related data.

In one implementation, Block S110 characterizes patient communicationbehavior prior to beginning a treatment regimen, and Block S160 selectsa set of other patients within a patient population who exhibit(ed)similar communication behaviors prior to beginning similar treatmentregimens. Block S160 can further select and/or filter the patientpopulation based on a diagnosis, therapy prescription, age, gender,location, and/or other demographic, activity behaviors prior tobeginning similar treatment programs, and/or other factors or variablessimilar to those of the patient. Block S160 can further select the setof other patients and/or update the set of other patients during thepatient's treatment regimen based on similar in-treatment communicationbehaviors (and other behaviors or variables) between the patient andother patients in the patient population.

Block S160 can therefore receive patient communication behaviorcharacterizations of the patient and other patients within the patientpopulation, such as from Block S110 and Block S120, to identifycommunication behaviors common to the patient and to the subgroup. BlockS160 can also select the subgroup as pertinent to the patient based ontreatment adherence data and/or trends and/or symptom data and/or trendscommon to the patient and the subgroup, such as before and/or during arelated treatment regimen. Similarly, Block S160 can select the subgroupbased on determined relationships between treatment adherence andcommunication data common to the patient and the other patients withinthe subgroup.

Block S210 of the second method, which recites identifying a populationof patients diagnosed with a health condition, and Block S220, whichrecites selecting a subgroup of patients within the populationexhibiting a similar behavioral characteristic, can implement similarfunctionality to identify a subset of patients within a patientpopulation that share commonalties, such as one or more diagnoses,health conditions, symptoms, treatment regimens, communication behaviorcharacteristics, treatment adherence characteristics, etc. For example,Block S210 can identify a population of patients prescribed a treatmentregimen for a particular diagnosed health condition, such as byidentifying anonymized patient profiles tagged with the particulardiagnosed health condition, and Block S220 can select a subgroup ofpatients from the population of patients based on similar communicationbehavior prior to initiation of the treatment regimen in Block S220.Blocks S230, S240, and S250 can therefore analyze and manipulatecommunication and/or survey data associated with selected patientswithin the subgroup (i.e., to estimate treatment regimen adherence, tocharacterize communication behavior, and to correlate communicationbehavior and health status of patients, respectively) before and/orafter the subgroup is selected in Block S220. However, Blocks S160,S210, and S220 can function in any other way to select a subgroup ofrelated patients from a population of patients.

8. Behavior Feature Engine

Any one or more of the foregoing Blocks of the first and second methodsS100, S200 can implement a Behavior Feature Engine (BFE) to handlepatient data before, during, and/or following a treatment regimen and tothen to manipulate patient data into an effective model.

The BFE includes a collection of statistics—a combination of uniquevariables or “features”—that vary amongst patients, conditions, anddisease states. A subset of features defined in the BFE can thus beimplemented to predict the health status of a patient, such as givencommunication behavior and/or treatment adherence of the patient. Themethods can collect raw patient data from the data collectionapplication described above, such as through Block S110, S112, etc. andcan then convert this raw data into statistical features to create theBFE, such as on a remote server for a subgroup of patients or locally ona mobile computing device for a particular patient.

For a particular patient, the BFE can extract independent features fromone of various types of data or modalities (e.g., phone calls, textmessages, instant messages). For example, the BFE can aggregate patientcommunication data, including a total number of calls made, received,accepted, missed, etc. and a total number of text (e.g., SMS text)messages sent, received, ignored, etc. on a particular day or withinanother period of time, as shown in FIG. 1A. From this data, the BFE canestimate an interaction balance for the patient, such as by calculatinga ratio of incoming communications (e.g., phone calls and text messages)to total communication within a period of time, as well as a patientresponsiveness, such as by calculating a number of “missed” interactionswithin the time frame based on a number of calls not accepted or textmessages ignored by the patient with the period of time.

The BFE can also calculate patient interaction diversity, including atotal number of individuals with whom the patient interacts, such asthrough a voice communication application or text messaging application,within the period of time. The BFE can further extract a patientmobility from patient location data, including an approximation of totalpatient movement during a particular phone call or during the total of acorresponding period of time, such as based on an estimated distancetraveled by foot or bike (excluding a distance traveled by car, train,etc.) based on GPS location data of the patient's mobile computingdevice (e.g., smartphone). The BFE can then generate a mobility radiusfactor—defining an approximate radius of an imaginary circleencompassing locations visited by a patient within a period of time—andtag or associate patient communication data with this mobility radiusfactor.

The BFE can also calculate other location data features, such as timespent at home, time spent at work, transition between home/work/sociallocations, isolation at a particular location for extended periods oftime, unpredictability (i.e., entropy) in location information, etc. TheBFE can also implement mobile phone usage, such as screen unlocks andapplication usage, to calculate features that summarize variations andperiods of patient activity and inactivity, identify extended periodsduring which the patient is hyperactive on the device, and/or determinesleep patterns.

The BFE can further handle other types of patient data. For example, theBFE can estimate an amount of physical activity undertaken by thepatient within the period of time based on motion sensor (e.g.,accelerometer) data recorded on the patient's mobile computing device orwearable device, as shown in FIG. 1B. In this example, the BFE can alsoimplement machine learning, such as feature extraction or patternmatching, to correlate motion data with a particular type of action,such as walking, playing tennis, or eating. The BFE can then associatecommunication data (e.g., times and types) with the activity data, suchas an amount of time the patient spent in phone calls during anactivity, set of activities, or period of time including one or moreparticular activities. However, the BFE can handle, manipulate, and/oraggregate any other type of patient data, such as any of the datacollected as described above.

The BFE can segment patient behavior to enable identification of patientbehavioral patterns. For example, the BFE can segment patient dataaccording to any suitable period of time or time frame, such as by wholeday, a preset number of hours, daytime and nighttime, weekdays andweekends, months, etc., and thus extrapolate an hourly, daily, weekly,or other time-dependent behavioral pattern. The BFE can similarlysegment patient behavior data by patient location, such as home, office,etc., and thus identify location-related patient behavioral patterns.

Feature extraction of patient data within the BFE can be driven bypredefined metrics, such as distinctness, aggregation, entropy, orpercentage change over time for a particular patient, and the BFE cangroup such features by time, patient, patient subgroup, etc. Featurescan be stored as standalone features or, as behavior changes, featuresgenerated through comparison of two or more standalone features fromdifferent times (or across a group or in comparison to a baselinefeature). The BFE can then extract a time-dependent behavior pattern fora patient or a subgroup of patients from these features. For example,the BFE can identify and measure statistically significant changes inpatient behavior throughout a treatment, including before, during,and/or after administration of the treatment (e.g., a pharmacologicaltreatment, physical therapy, etc.).

The BFE can also generate comparative representations of base patientbehavior features. For example, the BFE can compare a patient behaviorfeature value for a specific period of time against any one or more of abaseline (i.e., typical or normal) behavior for the patient as indicatedby patient data gathered over time, historical patient behavior data(e.g., at a similar times of day or days of a week), an expected orhealthy behavior as suggested by an expert, healthcare professional,literature, a past study, and/or an average or common behavior observedin patient population or patient subgroup exhibiting behaviors similarto the those of the patient. The BFE can output such comparativerepresentations in the form of differences, ratios, percentages, etc.and can handle the comparative representations as discrete features.

9. Predictive Modeling Engine

Blocks S130, S280, S284 of the first and second methods S100, S200recite generating models based on communication and survey data of oneor more patients. Generally, Blocks of the first and second methodsS100, S200 can implement the BFE in conjunction with a PredictiveModeling Engine (PME) to generate patient-, patient subgroup-, and/ortreatment-specific models.

Generally, the PME functions to identify and measure statisticallysignificant changes in patient behavior through periods of variedsymptom presentation or throughout a treatment, including before,during, and/or after administration of the treatment (e.g., apharmacological treatment, physical therapy, etc.), to generate a(patient- or subgroup-level) predictive model accordingly, and to trainthe predictive model with additional patient data over time. The PME canidentify correlations between patient behavior(s), symptoms, treatmentadherence, and other features extracted in the BFE to transform a largeamount of patient data into a predictive model defining links betweensuch observed patient data and associated efficacy, mitigation,abatement of patient symptoms and/or health condition via one or moretreatments (i.e., therapies), patient wellness, quality of life, and/orgeneral health of the patient.

The PME can therefore implement patient communication data to constructa patient behavior model from multiple patient-related features, such asincluding total call duration over a period of time (e.g., for a day,for a week), proportion of time spent on calls on weekdays and weekends(or days and nights), number of unique contacts in communication withthe patient, and percentage change in patient communication with certaincontacts over a baseline period of time. The PME can also associate suchfeatures—relative to the baseline—with patient treatment adherence,symptom presentation, and health condition progression to generate aholistic patient- or subgroup-specific treatment model.

In one example, the PME generates a model including a derived linkbetween patient health status and patient behavior features extracted inthe BFE by identifying and measuring statistically significant changesin behavior during days characterized by enhanced patient symptoms anddays characterized by relatively normal patient symptoms for anindividual patient or across a patient population subgroup. In thisexample, the PME can identify a correlation between low patient activityand enhanced symptoms and generate a predictive model accordingly. Suchdata can be applied to a particular patient, a patient subgroup, and/ora patient population to quantify how effectively a treatment enables apatient to be more active and return to a normal work-life balance incomparison to an alternative treatment. In another example, the PMEanalyzes behavior data of a bipolar patient to correlate patientbehaviors with patient symptoms. In this example, the PME and BFE cancooperate to identify baseline patient communication behavior andassociate this with a normal patient state, identify a period of lowpatient activity and isolated communication behavior and associate thiswith a depressive episode, and identify a period of patienthyper-activity, long work hours, and a high degree of socializing andassociate this with a manic episode. The PME can thus generate apatient-specific model relating patient behavior with normal, manic, anddepressive states. The PME can therefore manipulate changes in patientactivity or behavior, work, work-life balance, productivity, stress andanxiety, health and wellness, treatment outcome (e.g., self-reported bythe patient or entered by a caregiver, family-member, provider,employer, etc.), etc. to identify correlations between patient behaviorand patient health and build predictive models based on behavior change,treatment adherence, symptom presentation, and/or treatment outcomepatterns.

As described above, the PME can implement machine learning, data mining,and/or statistical approaches to generate one or more models specific toa patient, a patient subgroup, or a patient population. In particular,the PME can apply statistical approaches to inform an understanding ofunderlying patient behavioral data through distributions, correlations,hypothesis testing, etc. by pairing original features extraction withinthe BFE with clinical insight and domain-specific intuition, such asfrom a doctor, nurse, pharmacist, etc. The PME can apply featureselection approaches to determine a most predictive subset of features.For example, the PME can implement correlation-based feature selection(CFS), minimum redundancy maximum relevance (mRMR), Relief-F,symmetrical uncertainty, information gain, or other statistical methodsand statistic fitting techniques to select a set of features output fromthe BFE. The PME can also implement support vector machines, ensemblelearning (e.g., boosting, random forest), logistic regression, Bayesianlearning (e.g., wherein an outcome variable is a predefined class orcategory), or other machine learning techniques to predict patientbehavior, symptoms, treatment outcome, etc. In certain cases, additionalcomplexity can be added to this process. For example, the PME canimplement cost-sensitive learning and sampling approaches whendistribution of data between categories or classes is unequal, such aswhen a symptom of interest is depression, which is detectable within apatient on fewer than 10% of total days of a patient study.

In one implementation, the PME applies regression approaches andgeneralized linear models for model outputs that include continuousvalues, such as prediction of symptom severity. In anotherimplementation, the PME applies Support Vector Machine (SVM)classification approaches for model outputs that include discretevalues, such as for patient health risk prediction. In anotherimplementation, the PME applies clustering techniques for model outputsthat include a set of undefined clusters, such as a centroid-based,density-based, connectivity-based, and/or a distribution-based approachto define a cluster based on patient behaviors, as shown in FIG. 5. Forexample, the PME can apply clustering techniques for a model output thatspecifies a group of patients with similar symptoms or behaviorstogether. The PME can apply similar techniques to detect anomaliesand/or outliers in aberrant cases in collected data, such as patientswithin a population who do not respond to a treatment or show adverseeffects uncharacteristic of the greater patient population. The PME canfurther train a linear model by fitting a line to collected data orfeatures, and the PME can train a Bayesian network classification modeland/or a Gaussian mixture classification model throughExpectation-Maximization.

The PME can additionally or alternatively model subgroups of patientswith a population of patients. In one implementation, the PME identifiesa subset (i.e., subgroup) of patients with a certain degree ofsimilarity with one subset of patients in the population and a certaindegree of dissimilarity with another subset of patients in thepopulation, such as with respect to one or more behaviors. For example,for a particular therapy, the PME can select a first subgroup ofpatients who become more active during the therapy and a secondsubgroup—exclusive of the first subgroup—of patients who show littlemarked behavior change during the therapy. In this example, the PME canalso assign different intervention triggers, reminder types,notification triggers, etc. to each of the first and second subgroups.The PME can therefore apply behavior data passively collected from apatient population to identify clusters of related patients within thepopulation, as shown in FIG. 5.

As described above, the PME can generate models that output predictedpatient treatment adherence, patient symptom presentation, treatmentefficacy, patient response to a treatment, etc. However, the PME cangenerate models that additionally or alternatively incorporate or outputone or more of a risk of admission or re-admission to an associated careprovider, risk of a pending emergency room visit, risk of retrogressionof clinical symptoms, etc. A model generated through the PME cantherefore support or enable early detection of higher-risk patients,prediction of patient hospitalization or symptom relapse, and timelypatient intervention and messaging to improve or change patientbehaviors.

The PME can further train or update a model over time, such as when newdata from a specific patient or anonymized data from a patient subgroupbecomes available. For example, the PME can verify a previous predictionoutcome model for a specific patient in response to receiving feedbackfrom the patients. Alternatively, the PME associates the patient with analternative subgroup and selects an alternative model for the patient,accordingly, based on new patient feedback.

10. Health Risk Identification

Block S232 of the second method S200 of the method recites identifying arelationship between communication behaviors of patients within thesubgroup, characteristics of medical symptoms of patients within thesubgroup, and a treatment regimen administered to patients within thesubgroup. Block S280 of the second method S200 further recitesgenerating a health risk model for the subgroup based on therelationship, the health risk model defining a correlation between achange in communication behavior and risk of change in a medicalsymptom.

In this implementation of the first method, an individual patient andcan be diagnosed or at-risk for a certain condition, and Blocks of thefirst method can therefore estimate symptom severity, health status, orhealth risk change] for the patient based on volunteered survey resultsand/or an applicable health risk model for a corresponding subgroup ofpatients. Generally, the first method S100 can implement the health riskmodel generated in the second method S200 to predict a health risk of anindividual patient. In particular, Block S162 of the first method canretrieve the health risk model associated with the subgroup (anddefining a correlation between risk of change in a medical symptom andcommunication behavior for patients within the subgroup), and Block S172can predict a risk of change in a medical symptom for the patient basedon the log of use of the native communication application and the healthrisk model.

Blocks of the methods can therefore extract patient symptom severity orhealth status over time from corresponding survey responses collected incorresponding Blocks of the methods (e.g., Blocks S112, S122, etc.). Forexample, a survey delivered to a patient can include an explicit inquiryinto if and when a symptom occurred and a severity of symptompresentation, and Block S114 can analyze the patient's response to thesurvey to determine if and/or to what extent the patient symptomspresented for the corresponding time period. Block S114 can subsequentlypass the degree of symptom severity to Block S116. Block S124, BlockS230, etc. can implement similar functionality to deliver and assesspatient surveys.

Block S116 (and Block S126, etc.) can subsequently define a relationshipbetween a degree of symptom severity or health status and a qualityand/or quantity of communication (i.e., communication behavior, locationbehavior, and/or phone usage behavior) of the patient for acorresponding period of time. Block S116 can then pass this relationshipto Block S172 to predict symptom severity or health status for thepatient and/or for other patients in a corresponding subgroup.

In one implementation, Block S116 characterizes patient-provided symptomseverity and health status data from the first survey and associatesthis characterization with the communication behavior characterizationfrom Block S110. At a later time when the patient's communicationbehavior mimics the communication behavior during the first period, thefirst method S100 can thus anticipate the patient's symptom severity andhealth status at the later time to mimic the patient's treatmentbehavior during a corresponding time period. In one example, Block S116correlates a frequency of a first set of outgoing voice communicationsfrom a phone call application during the first period with high symptomseverity for the patient, and Block S126 correlates a frequency of asecond set of outgoing voice communications within the phone callapplication with low symptom severity for the patient, wherein thefrequency of the first set of outgoing voice communications is greaterthan the frequency of the second set of outgoing voice communications.

Block S116 can additionally or alternatively correlate patient symptomseverity or health status and/or patient communication behavior to achange in healthcare utilization, such as readmission or increasedhospital visits. In one example, Block S116 extracts a patient symptomseverity from the first survey and presentation of patient symptoms(e.g., how well the patient is feeling) with patient communicationbehavior (e.g., how, when, and who the patient contacts during a periodof time). In this example, Block S116 can determine that a period duringwhich the patient sends a low volume of text messages to a large breadthof contact types and engages in long phone conversations with a limitednumber of contacts corresponds to low symptom severity, and Block S126can determine that a period during which the patient sends a low volumeof text messages to a low number of unique contacts and engages in shortphone conversations with a single contact corresponds to a high symptomseverity. In this example, Block S116 can also correlate low symptomseverity with a certain amount or pattern of healthcare utilizationduring the first period (e.g., the patient did not have to visit thedoctor for anything other than regular checkups), and Block S126 canfurther correlate high symptom severity with increased healthcareutilization during the second period (e.g., the patient had to visit thedoctor several times beyond the regular visits).

The first method S100 can repeat elements of Blocks S110, S112, S114,S116, etc. to generate a set of communication behavior-symptom severity(and health status) features for the patient over time, and thesefeatures can be fed into Block S130, Block S160, Block S162, Block S144,Block S150, Block S172, and/or Block S180, etc. to enable furtherfunctionality.

Block S232 of the second method S200, which recites identifying arelationship between communication behaviors of patients within thesubgroup, characteristics of medical symptoms of patients within thesubgroup, and a treatment regimen administered to patients within thesubgroup, can implement similar functionality to generate communicationbehavior-symptom severity (and health status) features within thesubgroup of patients. For example, Block S232 can pair anonymizedcommunication data from various patients exhibiting similar behaviorsover time, such as while presenting low or high symptom severities, withsurvey responses from corresponding patients at corresponding times tooutput a collection of features specific to the subgroup. Block S232 canthus pass this collection of features to Block S280

In one implementation, Block S280 applies the collection of features toidentify trends or patterns between communication behavior, symptomseverity, and healthcare utilization across the population and generatesa corresponding intervention plan for the subgroup, as described below.

11. Health Risk Model

As described above, Blocks of the first and second methods S100, S200can implement the PME to generate patient- and/or subgroup-specificmodels.

In particular, Block S, which recites generating a health risk modelincluding the first log(e.g., frequency) of use of the nativecommunication application, the second log of use of the nativecommunication application, the first symptom severity or health status,and the second symptom severity or health status, can implement the PMEto generate a model linking patient communication behavior and healthrisk (e.g., symptom severity, risk of relapse, risk of hospitalreadmission, health status, etc.). For example, Block S130 can implementthe BFE to determine that low communication periods commonly followperiods in which the patient correlates with high symptom severity andthat period characterized by relatively high levels of patient activitycorrespond to low symptom severity. In this example, Block S130 can thenimplement the PME to generate a corresponding model specific to thepatient. Block S130 can subsequently pass this model to Block S144 toestimate a subsequent patient health risk at a subsequent time based onpatient behavior at the subsequent time. In particular, in this example,Block S144 can determine that the patient is at a high risk forincreased symptom severity if the patient's behavior (e.g.,communication behavior and/or activity level) substantially matchesrecorded patient behavior associated with that symptom severity (asdefined by the health risk model).

Block S162, which can recite retrieving a health risk model associatedwith the subgroup, can select a preexisting health risk (or riskidentification) model—generated by the PME and associated with asubgroup of patients—based on selection of the subgroup in Block S160.Block S144 can similarly function to pass subsequent patient behaviordata into the health risk model to predict a subsequent level of patientrisk for presenting symptom greater than a threshold severity. In thisvariation of the first method, Block S172 can additionally oralternatively predict short-term and/or long-term patient risk for highsymptom severity or change in health status or disease progression, suchas based on a change in communication behavior of the patient over acertain time period. For example, if the patient's total communicationsincrease during a certain time period and this increase incommunications is common for patients within the subgroup who show adecrease in symptom severity—as defined in the health risk model—BlockS172 can predict that the patient will exhibit a similar decrease insymptom severity.

However, Block S130 and Block S160 can function in any other way togenerate and retrieve a predictive model defining a relationship betweensymptom severity or health status and patient communication behaviorover time, respectively.

Block S172 can further estimate an efficacy of the patient intervention(e.g., nurse outreach, doctor-patient communication, automated healthtips delivered through the patient's mobile computing device) inimproving the health condition of the patient according to a comparisonbetween changes in symptom severity or health status of the patient.Block S172 can therefore implement elements of the BFE and/or the PME topredict the outcome of the intervention for the patient. For example,Block S172 can identify a pattern of communication behavior of thepatient, correlate the pattern of communication behavior withintervention outcomes, and then compare the interventions (e.g., basedon symptom presentation recorded in various survey responses entered bythe patient) to changes in healthcare utilization. In this example, ifthe patient is delivered relevant interventions but shows worsening orunchanged symptoms, Block S172 can estimate a low efficacy of theintervention in treating the patient's condition. Furthermore, in thisexample, Block S172 can compare the patient to existing health conditiondata and/or a health risk model for a subgroup of patients to determineif worsening symptoms despite delivering interventions is common orexpected for the patient's diagnosed health condition and/or for similarpatients (i.e., patients within the subgroup). Block S172 can thuspredict the efficacy of the interventions accordingly.

Block S260 of the second method S200 similarly recites estimating anefficacy of the interventions in improving the health condition forpatients within the subgroup based on of the delivery of interventionsto patients within the subgroup. Generally, Block S260 implementstechniques similar to Block S172 to apply elements of the BFE and/or thePME to predict the outcome of the interventions for the subgroup ofpatients within a patient population. For example, for patients withinthe selected subgroup, Block S260 can identify an increase in use ofnative communication applications by patients from prior to initiationof the interventions to the patients to during and after delivery of theinterventions to the patients, correlating the increase in use of thenative communication applications by the patients with improved healthstatus of the patients, and correlating improved health status of thepatients with efficacy of the interventions in improving the healthcondition of patients within the subgroups. In this example, Block S260can apply survey response and communication behavior of patients withinthe subgroup—and a known health condition common to patients within thesubgroup—to predict the efficacy of a nurse outreach program inproviding support for mental health issues of patients within thesubgroup. Additionally or alternatively, Block S260 can implement suchpatient data to estimate the efficacy of a health tips program inimproving health-related behaviors of diabetic patients within thesubgroup.

In one implementation, block S172 and Block S260 output quantitativevalues of intervention efficacy for the patient and the patientsubgroup, respectively. For example, Block S172 can output a predictedintervention efficacy of 80%, indicating that four out of five similarpatients show a reduction in symptom severity or an improvement inhealth status. As in this example, Block S172 can additionally output aconfidence interval indicating a statistical confidence in theintervention regimen, which can be dependent on a size of a patientsubgroup associated with an intervention efficacy model used to estimatetreatment efficacy for the patient or dependent on a predicted accuracyand/or availability of patient communication and survey response data,as shown in FIGS. 1A and 1C.

Alternatively, Block S172 and Block S260 output binary indicators ofintervention efficacy. For example, if predicted intervention efficacyfalls below a threshold, such as below a 70% predicted improvement rate,Block S172 and/or Block S260 can output a negative indicator ofintervention efficacy. In response to a negative predicted interventionefficacy, Block S172 can prompt Block S180 to automatically trigger anotification to care providers suggesting that a different interventionplan might better serve the patient. If data regarding past interventionsuccesses is available modification of the treatment regimen or prompt acare provider to modify the existing treatment regimen or to prescribe anew treatment regimen for the patient, as described below. Similarly, inresponse to a negative predicted treatment efficacy, Block S260 canprompt Block S284 to implement similar functionality to automaticallymodify or prompt manual modification of the treatment regimen for thesubgroup of patients.

As shown in FIG. 1C, one variation of the first method S100 includesBlock S180, which recites generating an updated intervention regimenaccording to the efficacy of the intervention regimen. Generally, BlockS180 functions to modify an intervention plan based on an interventionefficacy prediction output in Block S172. In one example, if thepatient's symptoms worsen over time when improvement is expected despitedelivering relevant interventions, Block S180 can prompt a doctor,pharmacologist, etc. to prescribe an alternative intervention program.

As shown in FIG. 2B, Block S284 of one variation of the second methodS200 similarly recites updating the intervention regimen for patientswithin the subgroup and currently prescribed the intervention regimen inaccordance with the intervention efficacy predicted in Block S260.

In one implementation of Block S284, the second method S200characterizes communication behavior of a patient following diagnosis ofa medical condition and before administration of an interventionprogram, applies the characterized communication behavior to select asubgroup of patients with similar diagnoses and communication behaviorsprior to administration of intervention programs, and selects anintervention program for the patient based on a predicted efficacy ofthe intervention for the patient, which is informed by an actualefficacy of the intervention within the selected subgroup (and/or acorresponding intervention efficacy model for the subgroup, as describedabove). Block S284 can function to pair a new patient to a subgroup ofcurrent and/or previous patients and to automatically prescribe anintervention regimen to the new patient accordingly.

12. Adherence Model

As described above, Blocks of the first and second methods S100, S200can implement the PME to generate patient- and/or subgroup-specificmodels.

In particular, Block S130, which recites generating a patient regimenadherence model including the first log of use of the nativecommunication application, the second log of use of the nativecommunication application, the first adherence, and the secondadherence, can implement the PME to generate a model linking patientcommunication behavior and treatment adherence. For example, Block S130can implement the BFE to determine that low communication periodscommonly follow periods in which the patient diverts from a prescribedtreatment regimen and that period characterized by relatively highlevels of patient activity correspond to suitable adherence to thetreatment regimen. In this example, Block S130 can then implement thePME to generate a corresponding model specific to the patient. BlockS130 can subsequently pass this model to Block S144 to estimate asubsequent patient treatment adherence at a subsequent time based onpatient behavior at the subsequent time. In particular, in this example,Block S144 can determine that the patient has effectively complied withhis prescribed treatment program if the patient's behavior (e.g.,communication behavior and/or activity level) substantially matchesrecorded patient behavior associated with treatment adherence compliance(as defined by the patient treatment adherence model), and Block S144can determine that patient has not suitably complied with his prescribedtreatment program when the patient's behavior substantially matchesrecorded patient behavior associated with treatment adherence neglect.

In this foregoing implementation, Block S144, which recites estimating athird adherence within the third time period based on the patientregimen adherence model and the third log of use of the nativecommunication application, functions to pass data from a subsequentthird period of time into the adherence model generated in Block S130 topredict patient adherence to the treatment regimen during the thirdperiod of time. Block S144 can additionally or alternatively estimate apatient response to a most-recent administration of the treatmentprogram, such as based on a patient response component of the treatmentadherence model.

Alternatively, Block S162, which recites retrieving a regimen adherencemodel associated with the subgroup, can select a preexisting treatmentadherence model—generated by the PME and associated with a subgroup ofpatients—based on selection of the subgroup in Block S160. Block S144can similarly function to pass subsequent patient behavior data into theregimen adherence model to predict a subsequent level of patientadherence to a prescribed treatment. In this variation of the firstmethod, Block S172 can additionally or alternatively predict short-termand/or long-term patient response to the treatment, such as based on achange in communication behavior of the patient soon after beginning atreatment program. For example, if the patient's total communicationsincrease soon after beginning a treatment regimen for a particularcondition and this increase in communications is common for patientswithin the subgroup who positively respond to the treatment regimen—asdefined in the regimen adherence model—Block S172 can predict that thepatient will exhibit a similar positive response to the treatmentregimen. Block S172 can similarly predict patient satisfaction in thetreatment regimen based on data of other patients within the subgroupand the regimen adherence model.

However, Block S130 and Block S160 can function in any other way togenerate and retrieve a predictive model defining a relationship betweentreatment regimen adherence and patient communication behavior overtime, respectively.

13. Treatment Regimen Model

As shown in FIG. 2B, one variation of the second method S200 includesBlock S280, which recites generating a treatment regimen model for thesubgroup, the treatment regimen model defining a correlation betweencommunication behavior, treatment responses, and treatment regimenoutcomes for patients within the subgroup. Generally, Block S280 canimplement the PME to generate a model correlating patient behavior topatient treatment outcome for a subgroup of patients. In one example,Block 242 can implement behavior data of a subsequent patient to matchthe subsequent patient to the subgroup, and Block S282 can feed behaviordata of the subsequent patient into the corresponding treatment regimenmodel to generate a predicted treatment regimen outcome for thesubsequent patient following administration of the treatment regimen asshown in FIG. 2B. Block S282 can similarly predict a health conditionrelapse risk for the subsequent patient based on the treatment regimenmodel, such as for the treatment regimen model that includes a relapserisk output component.

In one implementation, Block S280 implements the PME to generate atreatment regimen model for the subgroup based on adherence to thetreatment regimen, communication behavior, and survey responses ofpatients within the subgroup throughout a period of time. For example,Block S280 can implement the PME to generate the treatment regimen modelfor the subgroup by identifying patterns in communication behaviorduring administration of the treatment regimen and correlating patternsin communication behavior with treatment responses of patients withinthe subgroup. In this implementation, Block S282 can thus feed any oneor more of adherence to the treatment regimen, communication behavior,and a survey response of a subsequent patient into the treatment regimenmodel to output a predicted effect of the associated treatment on thesubsequent patient, such as for a particular period of time duringadministration of the treatment program to the subsequent patient.

However, Block S280 can function in any other way to generate apredictive model defining a relationship between treatment outcome for apatient and patient communication behavior over time.

14. Treatment Efficacy Model

Block S172, which recites estimating an efficacy of the treatmentregimen in treating the health condition of the patient according to acomparison between the treatment response and the adherence to thetreatment regimen by the patient, functions to implements elements ofthe BFE and/or the PME to predict the outcome of the treatment regimenfor the patient. For example, Block S172 can identify a pattern ofcommunication behavior of the patient, correlate the pattern ofcommunication behavior with a pattern of treatment adherence, and thencompare the pattern of treatment adherence to a trend in patienttreatment response (e.g., based on symptom presentation recorded invarious survey responses entered by the patient). In this example, ifthe patient exhibits strong treatment adherence but worsening symptoms,Block S172 can estimate a low efficacy of the treatment in treating thepatient's condition. Furthermore, in this example, Block S172 cancompare the patient to existing health condition data and/or a treatmentoutcome model for a subgroup of patients to determine if worseningsymptoms despite strong treatment adherence is common or expected forthe patient's diagnosed health condition and/or for similar patients(i.e., patients within the subgroup). Block S172 can thus predict theefficacy of the treatment accordingly.

Block S260 of the second method S200 similarly recites estimating anefficacy of the treatment regimen in treating the health condition forpatients within the subgroup based on adherence to prescribed treatmentregimens and health statuses of patients within the subgroup. Generally,Block S260 implements techniques similar to Block S172 to apply elementsof the BFE and/or the PME to predict the outcome of the treatmentregimen for the subgroup of patients within a patient population. Forexample, for patients within the selected subgroup, Block S260 canidentify an increase in use of native communication applications bypatients from prior to initiation of the treatment regimen by thepatients to during administration of the treatment regimen by thepatients, correlating the increase in use of the native communicationapplications by the patients with improved health status (e.g.,increased wellness, reduced symptoms) of the patients, and correlatingimproved health status of the patients with efficacy of the treatment intreating the health condition of patients within the subgroups. In thisexample, Block S260 can apply survey response and communication behaviorof patients within the subgroup—and a known health condition common topatients within the subgroup—to predict an efficacy of a physicaltherapy regimen in treating physical handicaps for patients within thesubgroup. Additionally or alternatively, Block S260 can implement suchpatient data to estimate the efficacy of a pharmacotherapy regimen intreating mental disorders for patients within the subgroup.

In one implementation, Block S172 and Block S260 output quantitativevalues—from a scale or spectrum of treatment values—of treatmentefficacy for the patient and the patient subgroup, respectively. Forexample, Block S172 can output a predicted treatment efficacy of 80%,indicating that four out of five similar patients recover from adiagnosed condition following the corresponding treatment regimen. As inthis example, Block S172 can additionally output a confidence intervalindicating a statistical confidence in the predicted treatment regimen,which can be dependent on a size of a patient subgroup associated with atreatment efficacy model used to estimate treatment efficacy for thepatient or dependent on a predicted accuracy and/or availability ofpatient communication and survey response data, as shown in FIGS. 1A and1C.

Alternatively, Block S172 and Block S260 output binary indicators oftreatment efficacy. For example, if predicted treatment efficacy fallsbelow a threshold efficacy, such as below a 70% predicted success rate,Block S172 and/or Block S260 can output a negative indicator oftreatment efficacy. In response to a negative predicted treatmentefficacy, Block S172 can prompt Block S180 to automatically triggermodification of the treatment regimen or prompt a care provider tomodify the existing treatment regimen or to prescribe a new treatmentregimen for the patient, as described below. Similarly, in response to anegative predicted treatment efficacy, Block S260 can prompt Block S284to implement similar functionality to automatically modify or promptmanual modification of the treatment regimen for the subgroup ofpatients.

As shown in FIG. 1C, one variation of the first method S100 includesBlock S180, which recites generating an updated treatment regimenaccording to the efficacy of the treatment regimen. Generally, BlockS180 functions to modify a treatment prescription based on a treatmentefficacy prediction output in Block S172. In one example, if the patientexhibits improved symptoms when a medication dosage is skipped orinadvertently reduced by the patient (or care giver, etc.), Block S180can respond to such improved symptoms by modifying the prescribedmedication dosage specified in the treatment regimen for the patient, asshown in FIG. 1C. In another example, if the patient's symptoms worsenover time when improvement is expected despite strong patient adherenceto the treatment program, Block S180 can cancel the current treatmentprogram and prescribe an alternative automatically or prompt a doctor,pharmacologist, etc. to prescribe an alternative treatment program.

As shown in FIG. 2B, Block S284 of one variation of the second methodS200 similarly recites updating the treatment regimen for patientswithin the subgroup and currently prescribed the treatment regimen inaccordance with the treatment efficacy predicted in Block S260.

In one implementation of Block S284, the second method S200characterizes communication behavior of a patient following diagnosis ofa medical condition and before administration of a treatment program,applies the characterized communication behavior to select a subgroup ofpatients with similar diagnoses and communication behaviors prior toadministration of treatment programs, and selects a treatment programfor the patient based on a predicted efficacy of the treatment for thepatient, which is informed by an actual efficacy of the treatment withinthe selected subgroup (and/or a corresponding treatment efficacy modelfor the subgroup, as described above). For example, Block S282 canprescribe a custom medication and custom dosage for the patient based onthe predicted treatment regimen outcome for the patient, such as bypassing different medications and/or dosages into the subgroup regimenoutcome model and selecting a particular medication and/or dosage thatyields a highest predicted treatment efficacy. Therefore, Block S284 canfunction to pair a new patient to a subgroup of current and/or previouspatients and to automatically prescribe a treatment regimen to the newpatient accordingly.

15. Notifications

Block S150 of the first method S100 recites presenting atreatment-related notification based on the third adherence through themobile computing device. Generally, Block S150 functions to generate andpresent a notification to the patient through the patient's mobilecomputing device to provide guidance to the patient during the treatmentprogram, such as in the form of a reminder to perform a particular task(shown in FIG. 1A), suggest or enforce a behavior change, providepositive reinforcement for a behavior, provide behavioral insights, orshare a patient symptom and/or behavior timeline. For example, onceBlock S144 identifies a period of low treatment adherence or treatmentneglect, Block S150 can generate a notification including a reminder totake a prescribed medication and transmit the reminder to the patient'smobile computing device, wherein a native application executing on themobile computing device displays the notification, such as in the formof a pop-up notification. Block S150 can transmit the notification tothe mobile computing device for display at a prescribed treatmentadministration time, such as a 9 AM and 5 PM—dosage times prescribed bya doctor—on a day following estimated treatment program neglect by thepatient. Alternatively, Block S150 can transmit the notification to thepatient's mobile computing device substantially in real time in responseto a patient action, inaction, behavior, or behavior change. Forexample, Block S150 can generate a notification reciting “everything allright?” and transmit the notification to the patient in response to adetected abrupt change in a patient behavior pattern (e.g., the patientcalls the same family member every morning at 9 AM, but 9:15 AM passeson a present day without an outgoing or incoming phone call).

Block S150 can also incorporate additional patient information in thenotification. For example, Block S150 can generate a notification thatspecifies a medication dosage for the patient, such as including howmany pills of a certain type (or size, shape, color, etc.). In thisexample, the notification can incorporate dosage information based on anupdated treatment regimen, such as output in Block S180. Block S150 canadditionally or alternatively generate the notification that includespast patient data indicating an expected outcome if the patient adheresto the treatment regimen and/or an expected outcome if the patientneglects the treatment regimen, such as based on a patient or subgrouptreatment adherence model.

Block S150 can generate the notification that includes a promptrequesting confirmation of patient implementation of the action itemspecified in the notification (e.g., the patient took his prescribedmedication). For example, the notification can recite “It's time to takeyour meds” and include input regions reciting “Roger that!” and “Remindme later,” as shown in FIG. 1A. Block S150 can subsequently pass a“Roger that!” selection back into the treatment adherence model of thepatient as adherence to the treatment program, and Block S150 can pass a“Remind me later” selection into the treatment adherence model asneglect or delay of the treatment program, as shown in FIG. 3.

Block S150 can additionally or alternatively interface with a careprovider, such as a nurse or doctor (e.g., through a care providerinterface executing on a corresponding computing device), to generate acustom notification. For example, Block S150 can transmit or sharepatient information, including health condition, treatment regimen,actual and estimated patient adherence, survey response, and/or actualand estimated treatment response data of the patient, with the careprovider and guide the care provider in identifying a patient problem orneed. Block S150 can then receive from the care provider a selection fora default notification from a pre-populated list of available and/orapplicable default notifications. Alternatively, Block S150 can promptthe care provider to enter a custom textual and/or image-based (e.g.,infographic) notification for the patient and then pass the notificationback to the patient. In one example, Block S172 predicts a risk ofchange in a medical symptom for the patient based on a risk of increasedsymptom severity for symptoms associated with a particular diseaseassociated with (e.g., diagnosed in) the selected subgroup, and BlockS150 transmits the notification to the care provider if to the risk ofchange in the medical symptom for the patient exceeds a threshold riskspecific to the particular disease, such as a 70% probability thatpatient symptom severity will systematically increase in the (near)future for a patient diagnosed with diabetes and an 88% probability thatpatient symptom severity will systematically increase in the (near)future for a patient diagnosed with ADHD.

In addition to generating a reminder for the patient, such as to adhereto a treatment program, Block S150 can additionally or alternativelygenerate a notification to prompt or reminder the patient to complete asurvey, schedule a visit with a doctor or health care provider, toattend a scheduled health-related visit, etc. However, Block S150 cangenerate a notification including any other information and presented tothe patient in any other suitable way or through any other suitablemedium.

16. Services

The first and second methods S100, S200 can function to provide one ormore services to a care provider associated with a patient and with asubgroup of patients, respectively. In one implementation, the first andsecond methods S100, S200 support a disease management program byenabling provision of lower cost treatments and/or therapies to patientsin exchange for feedback, wherein the first and second methods S100,S200 collect patient feedback as described above. In thisimplementation, the first method S100 can support tools and one or moremobile applications (i.e., native applications for mobile computingdevices) to enable better patient-driven condition management and toreduce symptom flare-ups, and the second method S200 can aggregate andextract valuable information from patient data across a subgroup tosupport disease management programs by a care provider. For example, thesecond method S200 can manage or provide analyzed data to a careprovider to manage a population with a chronic condition, such as bycollecting data about patients outside a clinical setting and generatereports and/or models to aid triage for higher-risk patients, and thefirst method S100—in conjunction within the second method S200—cansupport a communication venue or network to enable more effectiveoutreach from a care provider to a patient.

In one example, the first method S100 accesses a second log of use of anative communication application executing on a second mobile computingdevice by a second patient and predicts a second risk of change in amedical symptom for the second patient based on the second log of use ofthe native communication application and the health risk model, andBlock S150 transmits a scored patient triage list to a care provider(e.g., nurse, hospital, clinic, etc.) according to the risk of change inthe medical symptom for the patient and the second risk of change in themedical symptom for the second patient. In another example, Block S150transmits a notification to the care provider comprises prompting thecare provider to schedule a visit with a particular patient.

The first and second methods S100, S200 can therefore target patientinterventions to improve patient health and reduce healthcareutilization (e.g., care costs, emergency room visits, pharmaceuticalreliance, etc.). For example, the first and second methods S100, S200can generate and implement models to identify patients at higher-riskfor declining health, increased healthcare utilization, and higherhospital readmissions. Once such patients are identified, the first andsecond methods S100, S200 can prompt a care provider to intervene,thereby initiating targeted care interventions to improve patient healthand wellbeing.

The first and second methods S100, S200 can additionally oralternatively extrapolate insights from population-level data to guidecare provider improvement in practices, thereby guiding care providersto better patient outcomes. For example, the second method S200 candetect clusters of patients within a population who demonstrate similarbehavioral and outcome patterns, identify two clusters of patients whoshow significantly different readmission rates, identify differenttreatment practices across the two clusters, and thus guide a careprovider in designing a treatment practice based on the readmissionrates and the treatment practices across with the two clusters ofpatients.

In particular, various Blocks of the first and second methods S100, S200interface with care providers in various ways.

As described above, Block S172 can communicate a predicted (e.g.,estimated) efficacy of the treatment regimen for the patient to ahealthcare provider associated with the patient. For example, if thepredicted efficacy of the treatment falls below a threshold efficacy forthe health condition for the patient, Block S172 can transmit an alertor other notification to a doctor, nurse, or other care provider notedin the patient's medical file or recorded as managing treatment of theparticular health condition for the patient. In one implementation,Block S172 transmits an alert to a corresponding care provider through aprivate care provider account accessible through a provider-specificinterface (e.g., an online healthcare dashboard), such as a careapplication logged-in to a care provider's private account and executingon a computing device associated with the care provider. The firstmethod S100 can also enable the care provider to access additionalpatient information through the care application and/or private careaccount, such as through an online dashboard. Block S172 can also guidethe care provider in modifying an existing treatment program orselecting an alternative treatment program for the patient, as describedabove, such as by displaying a list of available treatment programs forthe patient's health condition in a scored list based on similaritiesbetween the patient's behaviors and behavior within a subgroup ofpatients and treatment efficacies within the subgroup (e.g., via atreatment efficacy model). However, Block S172 can function in any otherway to communicate a predicted efficacy of the treatment regimen for thepatient to a corresponding care provider.

Block S290 of the second method, which recites transmitting thepredicted treatment regimen outcome following administration of thetreatment regimen by the subsequent patient to a care provider specifiedin a digital health profile associated with the subsequent patient, canimplement similar techniques and/or methods to share health- andtreatment-related information to a care provider related to the(subsequent) patient. For example, Block S290 can transmit anotification including a prompt to respond to a current health status ofthe subsequent patient to a corresponding care provider. However, BlockS290 can function in any other way to communicate a predicted treatmentoutcome for a patient to a care provider.

Block S270 of the second method, which recites generating a treatmentregimen report specific to the subgroup based on the efficacy of thetreatment regimen, can implement similar techniques or functionality tocommunicate a subgroup-wide efficacy report for a particular treatmentregimen to a care provider. Generally, Block S270 functions to formulatea report for the efficacy of the treatment regimen with the populationbased on the estimated efficacy of the treatment regimen output in BlockS260. For example, Block S270 can generate the report that includescharts relating patterns in patent symptoms to treatment patterns intreatment adherence, changes in patterns in patient behaviors and surveyresponses, etc. for within the subgroup over time. In another example,Block S270 generates a treatment report specific to a particular patientwithin the subgroup, including a predicted risk of health conditiondecline for the particular patient during administration of thetreatment regimen, as shown in FIG. 2A. In this example, Block S270 canthus prompt a care provider to modify a treatment program for aparticular patient within a subgroup, such as based on predicted orextrapolated patient risk.

As in Block S172, Block S270 can also guide a care provider in modifyingor changing a treatment regimen for the subgroup, such as by identifyingand displaying (unexpected) patterns of treatment negligence or patient‘customization’ associated with improved patient symptoms and/or patientbehaviors within the subgroup. For example, the second method S200 canimplement behavior data of patients within the subgroup to compare thewellbeing of the patients and identify those most in need accordingly.Block S270 can thus generate the treatment regimen report that includesa ranking of patients within the subgroup based on need and symptoms.Thus, rather than randomly select patients within a subgroup to contactduring the treatment regimen, a nurse practitioner managing dozens ofpatients can review the treatment report for the subgroup to moreeffectively triage and determine the patients that are (likely) in needof support. Block S270—and the second method S200 in general—cantherefore enable a nurse or other care provider to make real-timedecisions and intervene and/or reach out to a particular patient basedon real patient data.

However, Block S270 can generate a report that includes any othersuitable patient information and can function in any other way to makethe report available to one or more care providers, such as related toone or more patients and/or a prescribed treatment regimen for thesubgroup.

As noted above, though the foregoing description describes theinventions particularly in the context of monitoring and/or estimatingtreatment adherence, the first and second methods S100, S200 canimplement similar functionality and techniques to identify patient riskand assess treatment efficacy.

The systems and methods of the embodiments can be embodied and/orimplemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a patient computer or mobiledevice, or any suitable combination thereof. Other systems and methodsof the embodiments can be embodied and/or implemented at least in partas a machine configured to receive a computer-readable medium storingcomputer-readable instructions. The instructions can be executed bycomputer-executable components integrated by computer-executablecomponents integrated with apparatuses and networks of the typedescribed above. The computer-readable medium can be stored on anysuitable computer readable media such as RAMs, ROMs, flash memory,EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or anysuitable device. The computer-executable component can be a processor,though any suitable dedicated hardware device can (alternatively oradditionally) execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the embodiments of the invention without departing fromthe scope of this invention as defined in the following claims.

We claim:
 1. A method for using mobility-related data to improve adherence determination for a patient during a treatment regimen, the method comprising: accessing a first log of user interaction with a user interface of a native communication application executing on a mobile computing device by the patient during the treatment regimen; generating a first patient interface comprising a first visible input region, the first patient interface defining a first digital survey for presentation at a display of the mobile computing device; collecting GPS data corresponding to a GPS sensor of the mobile computing device, the GPS data describing physical location of the mobile computing device and associated with location behavior of the patient during the treatment regimen; collecting motion data corresponding to an accelerometer system and a gyroscope system of the mobile computing device, the motion data describing physical orientation of the mobile computing device and associated with physical activity behavior of the patient during the treatment regimen; selecting a patient subgroup for the patient from a first subgroup and a second subgroup based on the GPS data and the motion data, wherein the first subgroup is selected in response to the physical location and the physical orientation of the mobile computing device indicating a first mobility behavior shared by the first subgroup, wherein the second subgroup is selected in response to the physical location and the physical orientation of the mobile computing device indicating a second mobility behavior shared by the second subgroup, and wherein selection of the patient subgroup is operable to improve data storage, data retrieval, and the adherence determination; automatically storing the first log of user interaction, the GPS data, and the motion data in association with the patient subgroup selected from the first and the second subgroups; retrieve a regimen adherence model based on the patient subgroup, the regimen adherence model defining a correlation between treatment regimen adherence and a communication behavior shared by the patient subgroup; estimating an adherence to the treatment regimen by the patient within the first time period based on the GPS data, the motion data, the regimen adherence module, and patient inputs collected during the first time period at the visible input region of the first patient interface; and promoting a treatment to the patient based on the patient adherence.
 2. The method of claim 1, wherein receiving the first survey response corresponding to the first time period comprises receiving a first symptom level of the patient within the first time period, and wherein estimating the first adherence to the treatment regimen by the patient comprises estimating the first adherence by the patient based on the first symptom level.
 3. The method of claim 1, wherein accessing the first log of use of the native communication application comprises quantifying a frequency and a duration of a first set of outgoing voice communications from a phone call application executing on the mobile computing device during the first time period.
 4. The method of claim 3, wherein accessing the first log of use of the native communication application comprises generating a weighted composite of the frequency, the duration, a timing, and a contact diversity of outgoing voice communications and a frequency, a duration, and a timing of incoming voice communications within the phone call application.
 5. The method of claim 3, wherein correlating the first log of use of the native communication application with the first adherence comprises correlating the frequency of the first set of outgoing voice communications with conformity to the treatment regimen by the patient, and wherein correlating the second log of use of the native communication application with the second adherence comprises correlating a frequency of a second set of outgoing voice communications within the phone call application with neglect of the treatment regimen by the patient, the frequency of the first set of outgoing voice communications greater than the frequency of the second set of outgoing voice communications.
 6. The method of claim 3, wherein accessing the first log of use of the native communication application further comprises quantifying the frequency of outgoing textual communications from a textual messaging application executing on the mobile computing device.
 7. The method of claim 1, wherein generating the patient regimen adherence model comprises identifying a change in use of the native communication application, a change in adherence to the treatment regimen between the first time period and the second time period, and a relationship between the change in use of the native communication application and the change in adherence to the treatment regimen and generating the patient regimen adherence model based on the relationship.
 8. The method of claim 7, further comprising estimating a first level of patient symptoms during the first time period from the first survey, estimating a second level of patient symptoms during the second time period from the second survey, and predicting an efficacy of the treatment regimen as treatment for the health condition of the patient based on the patient regimen adherence model, the first level of patient symptoms, and the second level of patient symptoms.
 9. The method of claim 1, wherein generating the patient regimen adherence model comprises selecting a subgroup of patients from a patient population based on the first log of use of the native communication application, the second log of use of the native communication application, and a communication behavior common to the subgroup and generating the regimen adherence model based on the communication behavior common to the subgroup and adherence to treatment regimens by patients within the subgroup.
 10. A method for calculating position of a GPS receiver during a communication period to calculate adherence of a patient to a treatment regimen, the patient associated with a health condition, the method comprising: collecting GPS satellite data received at the GPS receiver of a mobile computing device comprising the GPS receiver, a microprocessor, a display, and a wireless communication transceiver, wherein the GPS satellite data is received by the GPS receiver during the communication period; accessing a log of use of a native communication application executing on the mobile computing device by the patient during the communication period; wirelessly receiving the GPS satellite data and the log of use at a server from the mobile computing device, wherein the remote server comprises a central processing unit (CPU); calculating, by the server CPU, a set of features comprising a location feature and a communication behavior feature and a location feature, using the GPS satellite data and the log of use, wherein the set of features are configured to improve calculation of patient adherence to the treatment regimen; selecting a subgroup for the patient based on the log of use of the native communication application and a communication behavior common to the subgroup; retrieving, at the remote server, the regimen adherence model, wherein the regimen adherence model is associated with the subgroup and defines a correlation between treatment regimen adherence and communication behavior for patients within the subgroup; using the correlation to calculate, by the server CPU, the patient adherence associated with position of the GPS receiver during the communication period based on the communication behavior feature, the location feature, and the regimen adherence model; transmitting the patient adherence from the remote server to the mobile computing device; and displaying a visual representation of the patient adherence on the display of the mobile computing device.
 11. The method of claim 10, further comprising receiving from the patient a survey response corresponding to a time period associated with the log of use of a native communication application and extracting from the survey response a qualitative measure of adherence to the treatment regimen by the patient within the time period, wherein selecting the subgroup of the patient population comprises selecting the subgroup of the patient population further based on the qualitative measure of adherence to the treatment regimen by the patient that is similar to adherence to treatment regimens by patients within the subgroup.
 12. The method of claim 11, wherein retrieving the regimen adherence model comprises updating the regimen adherence model with the log of use of the native communication application and the survey response of the patient.
 13. The method of claim 10, wherein presenting the treatment-related notification comprises transmitting a custom notification from a healthcare provider to the mobile computing device and displaying the custom notification on a display of the mobile computing device.
 14. The method of claim 10, wherein accessing the log of use of the native communication application comprises generating a quantitative assessment of a frequency and a duration of outgoing phone calls and textual communications from the mobile computing device during a preset time period.
 15. A method comprising: providing a care provider interface to a care provider, wherein the care provider interface is accessible over the Internet by a care provider computing device; accessing a log of use of a native communication application executing on a mobile device associated with a patient, the log of use describing patient digital communication behavior during the treatment regimen; collect GPS data corresponding to a GPS sensor of the mobile device, the GPS data describing physical location of the mobile device and associated with location behavior of the patient during the treatment regimen; collect motion data corresponding to an accelerometer system and a gyroscope system of the mobile device, the motion data describing physical orientation of the mobile device and associated with physical activity behavior of the patient during the treatment regimen; selecting a patient subgroup from a first subgroup and a second subgroup based on the GPS data and the motion data, wherein the first subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating a first mobility behavior shared by the first subgroup, wherein the second subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating a second mobility behavior shared by the second subgroup, and wherein selection of the patient subgroup is operable to improve data storage, data retrieval, and the adherence determination; for the patient within the patient subgroup, estimating adherence of the patient to a prescribed treatment regimen during a period of time based on the log of use, the GPS data, the motion data, and survey responses entered by the patient through a corresponding mobile computing device; estimating an efficacy of the treatment regimen in treating the health condition for the patient within the patient subgroup based on adherence to prescribed treatment regimens and health statuses of patients within the patient subgroup; generating a treatment regimen report specific to the patient subgroup and enabling a wireless communicable link with a care provider computing device, based on the efficacy of the treatment regimen; and presenting, by way of the wireless communicable link with the care provider computing device, information derived from the treatment regimen report.
 16. The method of claim 15, further comprising, for a patient within the subgroup, updating a prescribed treatment regimen in accordance with the efficacy of the treatment regimen.
 17. The method of claim 15, further comprising characterizing communication behavior of a subsequent patient based on use of a native communication application executing on a corresponding mobile computing device by the subsequent patient, associating the subsequent patient with the subgroup based on the communication behavior of the subsequent patient, and prescribing the treatment regimen to the subsequent patient.
 18. The method of claim 15, wherein selecting the subgroup of patients comprises selecting a set of patients within the population based on similar patient communication behaviors within the set of patients prior to initiation of treatment regimens and during administration of the treatment regimens to the patients in the set of patients.
 19. The method of claim 18, wherein estimating the efficacy of the treatment regimen comprises, for a patient within the subgroup, identifying an increase in use of a native communication application by the patient from prior to initiation of the treatment regimen by the patient to during administration of the treatment regimen to the patient, correlating the increase in use of a corresponding native communication application by the patient with improved health status of the patient, and associating improved health status of the patient with efficacy of the treatment in treating the health condition of the patient.
 20. The method of claim 15, further comprising generating a treatment regimen model for the subgroup based on the adherence to the treatment regimen, communication behavior, and survey responses of patients within the subgroup throughout the period of time.
 21. The method of claim 15, wherein estimating the efficacy of the treatment regimen comprises estimating an efficacy of a physical therapy regimen in treating physical handicaps for patients within the subgroup.
 22. The method of claim 15, wherein estimating the efficacy of the treatment regimen comprises estimating an efficacy of a pharmacotherapy regimen in treating mental disorders for patients within the subgroup.
 23. The method of claim 15, wherein generating the treatment regimen report comprises predicting risk of decline in a health condition of a patient within the subgroup during administration of the treatment regimen to the patient.
 24. A system for using mobility-related data to improve adherence determination for a patient during a treatment regimen, the system comprising: a communication monitoring module operable to access a log of use of a native communication application executing on a mobile device associated with the patient, the log of use describing patient digital communication behavior during the treatment regimen; a mobility monitoring module operable to: collect GPS data corresponding to a GPS sensor of the mobile device, the GPS data describing physical location of the mobile device and associated with location behavior of the patient during the treatment regimen; and collect motion data corresponding to an accelerometer system and a gyroscope system of the mobile device, the motion data describing physical orientation of the mobile device and associated with physical activity behavior of the patient during the treatment regimen; and a processing system operable to: select a patient subgroup for the patient from a first subgroup and a second subgroup based on the GPS data and the motion data, wherein the first subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating a first mobility behavior shared by the first subgroup, wherein the second subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating a second mobility behavior shared by the second subgroup, and wherein selection of the patient subgroup is operable to improve data storage, data retrieval, and the adherence determination; automatically store the log of use dataset, the GPS data, and the motion data in association with the patient subgroup selected from the first and the second subgroups; retrieve a regimen adherence model based on the patient subgroup, the regimen adherence model defining a correlation between treatment regimen adherence and a communication behavior shared by the patient subgroup; predict patient adherence to the treatment regimen based on the log of use, the GPS data, the motion data, and the regimen adherence model; and promote a treatment to the patient based on the patient adherence.
 25. The system of claim 24, wherein the first mobility behavior comprises a low level of patient physical activity, wherein the first subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating a low level of patient physical activity shared by the first subgroup, wherein the second mobility behavior comprises a high level of patient physical activity, and wherein the second subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating a high level of patient physical activity shared by the second subgroup.
 26. The system of claim 24, wherein the first mobility behavior comprises a total patient movement below a threshold, wherein the first subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating movement of the patient being below the threshold, wherein the second mobility behavior comprises a total patient movement above the threshold, wherein the second subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating the movement of the patient being above the threshold.
 27. A method for improving processing of communication behavior data for predicting adherence of a patient to a treatment regimen, the patient associated with a health condition, the method comprising: accessing a log of use of a native communication application executing on a mobile computing device by the patient during a communication period; defining a subset of communication feature types for a subgroup of a patient population, wherein the subset of communication feature types are from a set of feature types, and wherein defining the subset of communication feature types is configured to improve processing of features by a remote server; determining an orientation of the mobile computing device in relation to the communication period based on signals from a set of inertial sensors mounted respectively at the mobile computing device during the communication period; extracting a patient mobility feature associated with the communication period from the orientation; extracting communication behavior features from the log of use, the communication behavior features typifying the subset of communication feature types; selecting the subgroup for the patient based on the log of use of the native communication application and a communication behavior common to the subgroup, wherein selecting the subgroup is configured to improve manipulation of the communication behavior features and retrieval of a regimen adherence model of a set of regimen adherence models by the remote server; retrieving, at the remote server, the regimen adherence model, wherein the regimen adherence model is associated with the subgroup and defines a correlation between treatment regimen adherence and communication behavior for patients within the subgroup; predicting, at the remote server, patient adherence to the treatment regimen based on the communication behavior features, the mobility feature, and the regimen adherence model; enabling a wireless communicable link with the mobile computing device; and presenting, by way of the wireless communicable link with the mobile computing device, a treatment-related notification at a care provider interface, based on the patient adherence through the mobile computing device. 